Creating a Data File. Open a data file in SPSS and enter the data presented in Table… 1 answer below »

Creating a Data File. Open a data file in SPSS and enter the data presented in Table 3.1 on page 72. Name this SPSS data file as LastnameFirstinitialSTAT8028-2a
Part B. Create a mock research project. Submit your answers to the three questions below in a Word doc. Name this SPSS data file as LastnameFirstinitialSTAT8028-2b
1. Considering your area of research interest, briefly state your area and a possible research project related to the area (150-500 words)
2. Pose one or more null and alternative hypotheses that follow from the possible research project.
3. List at least 10 variables that would be collected in your mock research project that would be used to answer the hypotheses. After each variable list the variable name you will use in SPSS (Part C), the level of measurement (binary, nominal, ordinal, interval, or ratio), and the possible range of scores. Feel free to be creative.
Part C. Create a mock SPSS data set. Name this SPSS data file as LastnameFirstinitial STAT8028-2c
1. Open a data file in SPSS and enter in a set of mock data for the research project you describe in Part B. (Note: It is important that you do not collect real data for this activity; you cannot collect data without IRB approval).
2. You must enter in 10 rows of data for all 10 variables (that is, create data for 10 mock participants).
3. Participant #1 must have missing data for Variable #3. Ensure this is coded correctly.
Activity #2b: Exploratory Data Analysis
Prior to conducting statistical tests that will evaluate your hypotheses, it is imperative to do what can be described as exploratory data analysis (EDA). Essentially, this entails visually examining your data and exploring, at a high level, any relationships intrinsic to the data. The end result is a comprehensive understanding of your data – this is a must prior to doing any hypothesis testing. Please remember this when you get to your dissertation. Spending time getting to know your data will expedite completion of your results sections.
Optional Preparation for Activity #2b
After completing the above activities, if you feel you need additional instruction on the concepts covered, please choose from any of the following activities that will assist you in mastering the core concepts.
Interactive Multiple Choice Questions. You might find it helpful to complete the multiple choice quizzes available at: http://www.sagepub.com/field3e/MCQ.htm
Flashcards. If what you need is gain a basic, definitional understanding of the topics, visit the Flashcard Glossary at: http://www.sagepub.com/field3e/Flashcard.htm
Activity #2b
Include the information in this activity with the Word document created for Activity #2a. You will create this Word document by cutting and pasting SPSS output into word.
Part A. Creating Visual Displays of Data. For this activity you will copy and paste output you created while working in Chapter 4 into a Word document. Please read the instructions below to ensure you are pasting the correct material into your activity document (this chapter has you create many charts and not all are required for Activity #2).
1. Using the data set: DownloadFestival.sav, create a boxplot for males and females for the variable Day1. It is important that you change the outlier identified to 2.02 prior to creating the boxplot. Be sure to save the data set with a new name, indicating it is the corrected data set (outlier identified and corrected). Save this boxplot, with an appropriate title in your Activity #2 Word document.
2. Using the data set: ChickFlick.sav, create a clustered bar chart for independent means. The variables you will use are: Arousal, Film, and Gender (grouping variable). Be sure to display error bars and save your chart with an appropriate title in your Activity #2 Word document.
3. Using the data set: Text Messages.sav (note: you may see an additional data set with the same name: TextMessages.sav – either will create the correct output), create a clustered bar chart for mixed designs. The variables you will use are: Time1, Time2, and Group. Be sure to display error bars, include labels for the X- and Y-axis, and save your chart with an appropriate title in your Activity #2 Word document.
4. Using the data set: Exam Anxiety.sav, create a scatterplot that includes a regression line. The variables you will use are: Exam Performance and Exam Anxiety. Be sure to include the regression line and save your chart with an appropriate title in your Activity #2 Word document.
Part B. Why Exploratory Data Analysis?
Write a short paragraph that highlights your understanding of why exploratory data analysis is a critical part of any analytical strategy (500 Word limit). This answer is worth 5 points (half the assigned points for this activity). To receive full credit you must show a high level of understanding the importance of exploring data visually.
Submit your files in the Course Work area below the Activity screen.
Learning Outcomes: 1, 3, 4, 10
? Review research methods and basic statistics as they relate to planning, conducting, and interpreting inferential statistics.
? Calculate, integrate, and evaluate descriptive statistical analysis.
? Create, integrate, and evaluate visual displays of data.
? Demonstrate proficiency in the use of SPSS.
Activity 3: Understanding and Exploring Assumptions (10 Points)
4Evaluation of Assumptions
Activity #3
You will submit one Word document for this activity. You will create this Word document by cutting and pasting SPSS output into word.
1. Why do we care whether the assumptions required for statistical tests are met? (You might want to write your answer on a note card you paste to your computer).
2. Open the data set that you corrected in activity #2 for DownloadFestival.sav. You will use the following variables: Day1, Day2, and Day3 (hygiene variable for all three days). Create a simple histogram for each variable. Choose to display the normal curve (under Element Properties) and title your charts. Copy these plots into your Activity #3 Word document.
3. Now create probability-probability (P-P) plots for each variable. This output will give you additional information. Read over the Case Processing Summary. Notice that there is missing data for Days 2 and Day 3? Copy only the Normal P-P Plots into your Activity #3 Word document (you do not need to copy the beginning output nor the Detrended Normal P-P Plots).
4. Examining the histograms and P-P plots describe the dataset, with particular attention toward the assumption of normality. For each day, do you think the responses are reasonably normally distributed? (just give your impression of the data). Why or why not?
5. Using the same dataset, and the Frequency command, calculate the standard descriptive measures (mean, median, mode, standard deviation, variance and range) as well as kurtosis and skew for all three hygiene variables. Paste your output into your Activity #4 Word document (you do not need to paste the Frequency Table). What does the output tell you? You will need to comment on: sample size, measures of central tendency and dispersion and well as kurtosis and skewness. You will need to either calculate z scores for skewness and kutosis or use those given in the book to provide a complete answer. Bottom line: is the assumption of normality met for these three variables? Does this match your visual observations from question #2?
6. Using the dataset SPSSExam.sav, and the Frequency command, calculate the standard descriptive statistics (mean, median, mode, standard deviation, variance and range) plus skew and kurtosis, and histograms with the normal curve on the following variables: Computer, Exam, Lecture, and Numeracy for the entire dataset. Complete the same analysis using University as a grouping variable. Paste your output into your Activity #3 Word document (you do not need to paste the Frequency Table). What do the results tell you with regard to whether the data is normally distributed?
7. Using the dataset SPSSExam.sav, determine whether the scores on computer literacy and percentage of lectures attended (with University as a grouping variable) meet the assumption of homogeneity of variance (use Levene’s test). You must remember to unclick the “split file” option used above before doing this test. What does the output tell you? (be as specific as possible).
8. Describe the assumptions of normality and homogeneity of variance. When these assumptions are violated, what are your options? Are there cases in which the assumptions may technically be violated, yet have no impact on your intended analyses? Explain.
Activity 4: Correlation and Regression (10 Points)
4Common Analytical Strategies
In Activity #3 you gained an understanding of the importance of examining assumptions prior to conducting statistical analyses that test hypotheses. In this activity you will move from descriptive analyses and the examination of assumptions to actually conducting such analyses. We will cover common analytical strategies : Correlation and Regression.
Correlation is a method used to express the relationship between two variables – that is, as one variable changes, how does the other? For example, you might be interested in studying whether there is a relationship between leadership styles and organizational effectiveness.
Regression is a method that uses one variable to predict another (continuous) variable. So, perhaps you are interested in studying stress and want to know if the number of hours spent in yoga can explain a significant amount of variance in stress scores. A simple regression can answer this question for you. For most research questions, you will want to add in other explanatory variables, like number of hours at work each week, how much caffeine they drink, personality style, etc. We will learn about multiple regression soon and mastering these simple techniques will lay a solid foundation for the more advanced ones (so don’t short change yourself on this activity).
Finally, this activity covers three chapters, so plan accordingly.
Activity #4
You will submit one Word document for this activity. You will create this Word document by cutting and pasting SPSS output into word. Please answer the questions first and include all output at the end of the activity in an Appendix.
Part A. SPSS Activity
Part A of Activity #4 has you really getting to know a set of data and allows you the opportunity to perform statistical tests and then interpret the output. You will rely on all you have learned to this point and add correlation and regression strategies to your tool kit.
Using the data set: Chamorro-Premuzic.sav you will focus on the variables related to Extroversion and Agreeableness (student and lecturer).
Do the following:
1. Exploratory Data Analysis.
a. Perform Exploratory Data Analysis on all variables in the data set. Because you are going to focus on Extroversion and Agreeableness, be sure to include scatterplots for these combinations of these variables (Student Agreeableness/Lecture Agreeableness; Student Extroversion/Lecture Extroversion; Student Agreeableness/Lecture Extroversion; Student Extroversion/Lecture Agreeableness) and include the regression line on the chart.
b. Give a one to two paragraph write up of the data once you have done this.
c. Create an APA style table that presents descriptive statistics for the sample.
2. Make a decision about the missing data. How are you going to handle it and why?
3. Correlation. Perform a correlational analysis on the following variables: Student Extroversion, Lecture Extroversion, Student Agreeableness, Lecture Agreeableness.
a. Ensure you handle missing data as you decided above.
b. State if you are using one or two-tailed test and why.
c. Write up the results APA style and interpret them.
4. Regression. Calculate a regression that examines whether or not you can predict if a student wants a lecturer to be extroverted using the student’s extroversion score.
a. Ensure you handle missing data as you decided above.
b. State if you are using one or two-tailed test and why.
c. Include diagnostics
d. Discuss assumptions; are they met?
e. Write the results in APA style and interpret it.
f. Does this result differ from the correlation result above?
5. Multiple Regression. Calculate a multiple regression that examines whether age, gender, and student’s extroversion and predict if a student wants the lecturer to be extroverted.
a. Ensure you handle missing data as you decided above.
b. State if you are using one or two-tailed test and why.
c. Include diagnostics
d. Discuss assumptions; are they met?
e. Write the results in APA style and interpret it.
f. Does this result differ from the correlation result above?
Part B. Applying Analytical Strategies to an Area of Research Interest
1. Briefly restate your research area of interest.
a. Pearson Correlation. Identify two variables for which you could calculate a Pearson correlation coefficient. Describe the variables and their scale of measurement. Now, assume you conducted a Pearson correlation and came up with a significant positive or negative value. Create a mock r value (for example, .3 or -.2). Report your mock finding in APA style (note the text does not use APA style) and interpret the statistic in terms of effect size and R2 while also taking into account the third variable problem and well as direction of causality.
b. Spearman’s Correlation. Identify two variables for which you could calculate a Spearman’s correlation coefficient. Describe the variables and their scale of measurement. Now, assume you conducted a correlation and came up with a significant positive or negative value. Create a mock r value (for example, .3 or -.2). Report your mock finding APA style (note the text does not use APA style) and interpret the statistic in terms of effect size and R2 while also taking into account the third variable problem and well as direction of causality.
c. Partial Correlation vs. Semi-Partial Correlation. Identify three variables for which you may be interested calculating either a partial or semi-partial correlation coefficient. Compare/contrast these two types of analyses, using your variables and research example. Which would you use and why?
d. Simple Regression. Identify two variables for which you could calculate a simple regression. Describe the variables and their scale of measurement. Which variable would you include as the predictor variable and which as the outcome variable? Why? What would R2 tell you about the relationship between the two variables?
e. Multiple Regression. Identify at least 3 variables for which you could calculate a multiple regression. Describe the variables and their scale of measurement. Which variables would you include as the predictor variables and which as the outcome variable? Why? Which regression method would you use and why? What would R2 and adjusted R2 tell you about the relationship between the variables?
f. Logistic Regression. Identify at least 3 variables for which you could calculate a logistic regression. Describe the variables and their scale of measurement. Which variables would you include as the predictor variables and which as the outcome variable? Why? Which regression method would you use and why? What would the output tell you about the relationship between the variables?
Activity 5: t test and ANOVA (10 Points)
4Conducting Tests
In Activity #4 you learned how to examine relationships between variables, conduct analyses related to correlation and regression, and interpret the output associated with each.
In this section you will learn how to conduct tests that determine if there are differences between mean scores for groups. For example, you might be interested in studying whether there are mean differences in heart rate between two groups: those who exercise and those who do not (t-test), or you might a slightly more complicated design and compare mean heart rates for three groups: non-exercisers, occasional exercisers, and regularly exercisers (ANOVA).
Activity #5
You will submit one Word document and one SPSS data file for this activity. You will create the Word document by cutting and pasting SPSS output into word. Please read the instructions below to ensure you are pasting the correct material into your activity document. The Word document will be named LastnamefirstinitialSTAT8028-5a and the SPSS document as -5b.
Part A. Dependent t-test
In this activity, we are interested in finding out whether participation in a creative writing course results in increased scores of a creativity assessment. For this part of the activity, you will be using the data file “Activity 5a.sav”. In this file, “Participant” is the numeric student identifier, “CreativityPre” contains creativity pre-test scores, and “CreativityPost” contains creativity post-test scores. A total of 40 students completed the pre-test, took the creativity course, and then took the post-test.
1. Exploratory Data Analysis/Hypotheses.
a. Perform exploratory data analysis on CreativityPre and CreativityPost. Using SPSS, calculate the mean and standard deviation of these two variables.
b. Construct an appropriate chart/graph that displays the relevant information for these two variables.
c. Write the null and alternative hypotheses used to test the question above (e.g., whether participation in the course affects writing scores).
2. Comparison of Means
a. Perform a dependent t-test to assess your hypotheses above (note that many versions of SPSS use the term “paired samples t-test” rather than dependent t-test; the test itself is the same.
b. Write one or two paragraphs that describe the dataset, gives your hypothesis, and presents the results of the dependent sample t-test. Be sure that your writing conforms to APA style.
Part B. Independent t-test
In this activity, we will start with the data file used in Part A (“Activity 5a.sav”). Suppose, however, you [the researcher] encountered a small problem during data collection: after the post-tests were collected, you realized that the post-test form did not ask for the students’ identification number. As such, it will be impossible to match pre-test scores to post-test scores. Rather than simply give up, you start thinking about the data you do have, and try to determine whether you can salvage your project. In assessing the situation, you realize that you have 40 pre-test scores and 40 post-test scores, but no way to link them. While it will result in a weaker comparison, you determine that you are still able to compare pre-test vs. post-test scores; you will use a between-subjects design rather than a within-subjects design.
1. Create the data set.
a. Using the “Activity 5a.sav” file as a starting point, create a new dataset that you can use with the between subjects design. Hint: you will no longer need the variables CreativePre and CreativeTest. Instead, you have only one variable for the score on the creativity test. A second (or grouping) variable will serve to indicate which test the student took.
b. Submit the dataset as one of the Activity 5 files.
2. Exploratory Data Analysis/Hypotheses.
a. Perform exploratory data analysis on CreativityPre and CreativityPost. Using SPSS, calculate the mean and standard deviation of these two variables.
b. Construct an appropriate chart/graph that displays the relevant information for these two variables.
c. Write the null and alternative hypotheses used to test the question above (e.g., whether participation in the course affects writing scores).
3. Comparison of Means
a. Perform an independent t-test to assess your hypotheses above (note that many versions of SPSS use the term “independent samples t-test” rather than simply “independent t-test”.
b. Write one or two paragraphs that describe the dataset, gives your hypothesis, and presents the results of the dependent sample t-test. Be sure that your writing conforms to APA style.
4. Comparison of Designs
a. In this activity you used the same dataset to analyze both a between- and within-subjects design. Create a single paragraph (using the material you wrote above), that presents both sets of results.
b. Explain, in 300-500 words, whether the two tests resulted in the same findings. Did you expect this to be the case? Why or why not? What have you learned in this activity?
Part C. ANOVA
All of us have had our blood pressure measured while at our physician’s office. How accurate are these measurements? It may surprise you to learn that there is something called “White coat syndrome”—the tendency of some people to exhibit elevated blood pressure in clinical (medical) settings only. In other words, for these people the very fact that the physician is taking their blood pressure causes it to increase (for more information about white coat syndrome see http://www.webmd.com/anxiety-panic/features/beyond-white-coat-syndrome). In this activity, you will be using the “Activity 5c.sav” data file to determine whether you find support for the existence of white coat syndrome. In this study, 60 participants were randomly assigned to one of three groups. The “settings” variable indicates the location in which the participant’s blood pressure was recorded: 1=home, 2=in a doctor’s office, and 3=in a classroom setting. The “SystolicBP” variable contains the participant’s systolic pressure (the “upper” number). The “DiastolicBP” variable contains the participant’s diastolic pressure (the “lower” number).
1. Exploratory Data Analysis/Hypotheses.
a. Perform exploratory data analysis on both the SystolicBP and DiastolicBP variables. Using SPSS, calculate the mean and standard deviation of these two variables. Be sure that your analysis is broken down by setting (e.g., you will have six means, six SD’s, etc.).
b. Create two graphs—one for systolic and one for diastolic pressure. Each graph should clearly delineate the three groups.
c. Write a null and alternative hypothesis for the comparison of the three groups (note that your hypothesis will state that the three groups are equivalent; be sure to word your null hypothesis correctly).
2.ANOVA.
a. Using the “Activity 5c.sav” data file, perform two single factor ANOVAs: one using SystolicBP and one using DiastolicBP as the dependent variable.
b. If appropriate for either or both of the ANOVAs, perform post hoc analyses to determine which groups actually differ.
c. Write one paragraph for each ANOVA (be sure to use APA style). At a bare minimum, each paragraph should contain the three means, three SD’s, ANOVA results (F, df), post hoc tests (if applicable), effect size, and an interpretation of these results.
Section 3: Advanced Statistical Techniques
Activity 6: ANCOVA & Factorial ANOVA (10 Points)
4Advanced Techniques
In Activity #5 you learned how to conduct tests that determine if there are differences between mean scores for groups. However, many research questions require more complex designs that include the ability to control for confounding variables and/or include multiple independent variables. For example: You are interested in outcomes for three different leadership styles and want to control for the type of personality. Or: You want to examine attitudes towards a new federal law and believe that political affiliation and gender are relevant factors to consider.
In this activity you will learn these advanced techniques.
Activity #6
You will submit one Word document for this activity. You will create this Word document by cutting and pasting SPSS output into word. Activity #6 consists of two parts. In the first part, you will utilize an existing dataset to compute a factorial ANOVA. All SPSS output should be pasted into your Word document. In the second part, you will be asked to create a hypothetical ANCOVA output table (for variables related to your area of interest).
Part A. SPSS Activity
The “Activity 6.sav” file contains a dataset of a researcher interested in finding the best way to educate elementary age children in mathematics. In particular, she thinks that 5th grade girls do better in small class sizes while boys excel in larger classes. Through the school district, she has arranged a pilot program in which some classroom sizes are reduced prior to the state-wide mathematics competency assessment. In the dataset, you will find the following variables:
Participant: unique identifier
Gender: Male (M) or Female (F)
Classroom:
Small (1) – no more than 10 children
Medium (2) – between 11 and 19 children
Large (3) – 20 or more students
Score – final score on the statewide competency assessment.
In Activity #6, do the following:
1. Exploratory Data Analysis.
a. Perform exploratory data analysis on all variables in the data set. Realizing that you have six groups, be sure that your exploratory analysis is broken down by group. When possible, include appropriate graphs to help illustrate the dataset.
b. Give a one to two paragraph write up of the data once you have done this.
c. Create an APA style table that presents descriptive statistics for the sample.
2. Factorial ANOVA. Perform a factorial ANOVA using the “Activity 6.sav” data set.
a. Is there a main effect of gender? If so, explain the effect. Use post hoc tests when necessary (or explain why they are not required in this specific case).
b. Is there a main effect of classroom size? If so, explain the effect. Use post hoc tests when necessary (or explain why they are not required in this specific case).
c. Is there an interaction between your two variables? If so, using post hoc tests, describe these differences.
d. Is there support for the researcher’s hypothesis that girls would do better than boys in classrooms with fewer students? Explain your answer.
e. Write up the results APA style and interpret them. Be sure that you discuss both main effects and the presence/absence of an interaction between the two.
Part B. Applying Analytical Strategies to an Area of Research Interest
3. Briefly restate your research area of interest.
Analysis of Covariance. Using your area of interest, identify one independent and two dependent variables, such that the dependent variables would likely be covariates. Now, assume you conducted an ANCOVA that shows both the first independent variable as well as the covariate significantly predicts the dependent variable. Create a mock ANCOVA output table (see SPSS Output 11.3 in your text for an example) that supports the relationship shown above. Report your mock finding APA style.
Activity 7: Non-Parametric Tests (10 Points)
4Non-Parametric Tests
While you have learned a number of parametric statistical techniques, you are also aware that if the assumptions related to the tests are violated, then the tests are not valid. Because many phenomenon examined in business are not normally distributed, it is critically important to understand the role of non-parametric tests. It is possible you will need to use one or more of the methods covered in this chapter in your dissertation.
Activity #7
You will submit one Word document for this activity. In the first part your activity #7 document, provide short answers to the following questions (250 words or less).
Part A. Questions about non-parametric procedures
1. What are the most common reasons you would select a non-parametric test over the parametric alternative?
2. Discuss the issue of statistical power in non-parametric tests (as compared to their parametric counterparts). Which type tends to be more powerful? Why?
3. For each of the following parametric tests, identify the appropriate non-parametric counterpart:
a. Dependent t-test
b. Independent samples t-test
c. Repeated measures ANOVA (one-variable)
d. One-way ANOVA (independent)
e. Pearson Correlation
Part B. SPSS Activity
In this part of Activity #7, you will perform the non-parametric version of the tests you used in previous activities. In each case, assume that you opted to use the non-parametric equivalent rather than the parametric test. Using the data files from earlier activities, complete the following tests and paste your results into the assignment Word document:
1. Activity 5a: non-parametric version of the dependent t-test
2. Activity 5b: non-parametric version of the independent t-test
3. Activity 5c: non-parametric version of the single factor ANOVA
4. Activity 6: non-parametric version of the factorial ANOVA
Part C. Contingency tables
Sometimes a researcher is only interested in the following: Whether or not two variables are dependent on one another, (e.g. are death and smoking dependent variables; are SAT scores and high school grades independent variables?)
To test this type of claim a contingency table could be used, with the null hypothesis being that the variables are independent. Setting up a contingency table is easy; the rows are one variable the columns another. In contingency table analysis (also called two-way ANOVA) the researcher determines how closely the amount in each cell coincides with the expected value of each cell if the two variables were independent.
The following contingency table lists the response to a bill pertaining to gun control.

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In favor Opposed
Northeast 10 30
Southeast 15 25
Northwest 35 10
Southwest 10 25

Notice that cell 1 indicates that 10 people in the Northeast were in favor of the bill.
Example: In the previous contingency table, 40 out of 160 (1/4) of those surveyed were from the Northeast. If the two variables were independent, you would expect 1/2 of that amount (20) to be in favor of the amendment since there were only two choices. We would be checking to see if the observed value of 10 was significantly different from the expected value of 20.
To determine how close the expected values are to the actual values, the test statistic chi-square is determined. Small values of chi-square support the claim of independence between the two variables. That is, chi-square will be small when observed and expected frequencies are close. Large values of chi-square would cause the null hypothesis to be rejected and reflect significant differences between observed and expected frequencies. This part of the activity is not included in the text book. See the tutorial Chi-square pdf file in the additional resources section of the course room for details on how to perform a chi-square test in SPSS.
For part C, download the gss.sav file, and following the steps described in the Chi-Square tutorial.pdf (both located in the additional resources section of the course room), examine the relationship between education (degree) and perception of life (life). Can you reject the null that education and perception of life are independent? Make a bar chart that graphically summarizes your findings. Be sure to include the relevant portions of the chi-square test output in your explanation.
Activity 8: Signature Assignment (30 Points)
4Signature Assignment
For the final activity, please thoroughly answer each of the questions below. Your grade on this activity will be based on accuracy and comprehensiveness. Your paper should be between 3500-4200 words using APA formatting.
Review the file education.sav. Using the data contained in this 500 sample data set, synthesize an integrated understanding about education in four different areas. In this assignment, you will need to examine the data, determine the appropriate test method being sure that the conditions required for that method have been met, perform the analysis, then interpret the results. Synthesize your findings into an integrated report. Be sure to support your position with data and the appropriate statistical tests as needed. Locate two peer review journal articles that deal with each question; compare and contrast your findings with the peer review research. Prepare a paper suitable for submission to a non-statistician academic conference on adult education using graphs, tables, and figures as necessary while still maintaining appropriate academic rigor. Place all relevant statistical output in an appendix.
1. What is the relationship, if any between education and gender? Discuss any differences that may exist and describe the characteristics of the sample.
2. What is the relationship, if any, between parental education and the education of the respondent? If a relationship exists, which parent has the strongest effect on the educational level of the respondent?
3. Is there a linear relationship between age and education, and if so, how strong is that relationship? Is it possible to predict educational level based on age? If so, what limitations exist for the developed method?
4. What is the relationship of marital status on education? Do singles or married persons tend to be more highly educated?

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