Please see attached

Descriptive Statistics Can you predict clothing sales by number of catalogs mailed out to customers? Mean Std. Deviation N Sales of Women's Clothing 40583.6799 12196.43013 120 Hypotheses: In a brief paragraph, just say your impression of the question. If you had to make Number of Catalogs Mailed 10131.77 697.898 120 an educated guess, based on your own real-life experience (and previous SPSS assignments), do + you think number of catalogs mailed out to customers would be a good predictor of clothing Correlations sales? Why? Sales of Women's Number of Clothing Catalogs Mailed SPSS Analysis: We have a data set with two variables, number of catalogs mailed out to Pearson Correlation Sales of Women's Clothing 1.000 .681 customers (continuous), and the sales of women's clothing within that catalog (also a Number of Catalogs Mailed .681 1.000 :.001 continuous variable). Sig. (1-tailed) Sales of Women's Clothing Number of Catalogs Mailed 000 N Sales of Women's Clothing 120 120 Results: Identify the primary goal of this analysis. Report the coefficient of determination (r- Number of Catalogs Mailed 120 120 squared). Why is this statistic important to this output? Report the F-ratio. Why is this statistic important to this output? Report the t-value (and it's significance) for the slope. Why is this Variables Entered/Removeda statistic important to this output? Report the regression equation. Variables Variables Mode Entered Removed Method Conclusions: Write a brief paragraph about what your results indicate regarding clothing sales Number of Enter for this company. Can you trust the regression line produced from this analysis? Provide Catalogs Mailedb a. Dependent Variable: Sales of Women's Clothing evidence to support your answer. d variables entered Regression Equation Model Summary Y= bxta Adjusted R Std. Error of the R Y= (4.894x) + -8999.716 Model R Square Square Estimate 1 681 464 .460 8965.74245 a. Predictors: (Constant), Number of Catalogs Mailed ANOVA Model Sum of Squares df Mean Square F Sig Regression 8216220613.18 1 8216220613.184 102.211 <.001b residual tota a. dependent variable: sales of women clothing b. predictors: number catalogs mailed coefficientsa standardized unstandardized coefficients model b std. error beta sig. .073 .484 .681>