Assignment: Through analysis of the SPSS output, answer the following questions.

Question

Assignment: Through analysis of the SPSS output, answer the following questions. 1. What is the total sample size? 2. What is the mean income and mean number of hours worked? 3. 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.001). Based on these results, does the model fit the data? Briefly explain. (Hint: A signifi 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)? 4. 5. 6. 9 icant finding indicates good 7.

Explanation / Answer

1. The SPSS output for descriptive statistcs shows that the sample size, N=378.

2. The descriptive statistics show that mean income is $1485.49 and mean hours worked per week in current job is 33.52 hours.

3. Look into the correlations output box. The outcome variable is family income prior month all sources and predictor variable is hours worked per week in current job. The correlation coeffciient between outcome and predictor variable is 0.300. The significant (1-tailed) value is 0.000. Per rejection rule based on p value, reject null hypothesis if p value is less than alpha=0.05. Hence, reject null hypothesis (H0: family income prior month all sources and hours worked per week in current job are linearly uncorreletaed) and conclude that correlation coefficient is statistically significant. The correlation coefficient has positive sign, which indicates that the variables have a positive relationship, that is with increase in hours worked per week in current job, the family income prior month all sources will increase. The strength of relationship is determined by effect size, which in turn is a mesure of r. From Cohen's description of strength of relationship based on effect size, a value of r=0.3 holds moderately strong relationship (0.1-week, 0.3-moderate, 0.5 above -strong).

4. The value of coefficient of determination, r^2 is 0.090, which implies that around 9% variation in family income prior month all sources is explained by the least sqaure model.

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