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Review the Statistics and Data Analysis for Nursing Research chapters that you read as a part of the Week 7 Learning Resources. As you do so, pay close attention to the examples presented—they provide...

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  • Review the Statistics and Data Analysis for Nursing Research chapters that you read as a part of the Week 7 Learning Resources. As you do so, pay close attention to the examples presented—they provide information that will be useful for you to recall when completing the software exercises. You may also wish to review the Research Methods for Evidence-Based Practice video resources.
  • Refer to the Week 7 Linear Regression Exercises page and follow the directions to calculate linear regression information using the Polit2SetA.sav data set.
  • Compare your data output against the tables presented on the Week 7 Linear Regression Exercises SPSS Output document.
  • Formulate an initial interpretation of the meaning or implication of your calculations.

To complete:

  • Complete the “Simple Regression” and “Multiple Regression” steps and Assignments as outlined in the Week 7 Linear Regression Exercises page.
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Week 7 Linear Regression ExercisesSimple RegressionResearch Question: Does the number of hours worked per week (workweek) predict family income (income)? Using Polit2SetA data set, run a simple regression using Family Income (income) as the outcome variable (Y) and Number of Hours Worked per Week (workweek) as the independent variable (X). When conducting any regression analysis, the dependent (outcome) variables is always (Y) and is placed on the y-axis, and the independent (predictor) variable is always (X) and is placed on the x-axis. Follow these steps when using SPSS:Open Polit2SetA data set. Click on Analyze, then click on Regression, then Linear.Move the dependent variable (income) in the box labeled “Dependent” by clicking the arrow button. The dependent variable is a continuous variable. Move the independent variable (workweek) into the box labeled “Independent.” Click on the Statistics button (right side of box) and click on Descriptives, Estimates, Confidence Interval (should be 95%), and Model Fit, then click on Continue.Click on OK. Assignment: Through analysis of the SPSS output, answer the following questions.What is the total sample size?What is the mean income and mean number of hours worked? What is the correlation coefficient between the outcome and predictor variables? Is it significant? How would you describe the strength and direction of the relationship?What it the value of R squared (coefficient of determination)? Interpret the value.Interpret the standard error of the estimate? What information does this value provide to the researcher? The model fit is determined by the ANOVA table results (F statistic = 37.226, 1,376 degrees of freedom, and the p value is XXXXXXXXXXBased on these results, does the model fit the data? Briefly explain. (Hint: A significant finding indicates good model fit.)Based on the coefficients, what is the value of the y-intercept (point at which the line of best fit crosses the y-axis)? Based on...

Answered Same DayDec 27, 2021

Solution

Robert answered on Dec 27 2021
47 Votes
Week 7 Linear Regression Exercises
Week 7 Linear Regression Exercises
Simple Regression
Assignment: Through analysis of the SPSS output, answer the following questions.
1. What is the total sample size?
378
2. What is the mean income and mean number of hours worked?
mean income = $1,485.49
mean number of hours worked = 33.52
3. What is the co
elation coefficient between the outcome and predictor variables? Is it significant? How would you describe the strength and direction of the relationship?
co
elation coefficient between income and number of hours worked = .300
With r = .3 and p-value < 0.05, I reject ho and conclude that value of co
elation coefficient between income and number of hours worked is significant.
with t = 0.3 I can say that there is a weak positive linear relaitonship between income and number of hours worked. that is as the value of number of hours worked increases the value of income also increases.
4. What it the value of R squared (coefficient of determination)? Interpret the value.
R square = 0.090
this implies only 9% variation in income is explained by number of hours worked.
5. Interpret the standard e
or of the estimate? What information does this value provide to the researcher?
SE = $907.877. It is a measure of the variability of predictions in a regression.
6. The model fit is determined by the ANOVA...
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