Instructions This week's lab will use regression to answer the question: Can you predict clothing sales by number of catalogs mailed out to customers? Hypotheses: In a brief paragraph, just say your impression of the question. If you had to make an educated guess, based on your own reallife experience {and previous SPSS assignments), do you think number of catalogs mailed out to customers would be a good predictor of clothing sales? Why? SPSS Analysis: We have a data set with two variables. number of catalogs mailed out to customers {continuous}, and the sales of women's clothing within that catalog (also a continuous variable}. Open the data {see Dataset Files below). Conduct a simple regression to determine the ability of number of catalogs mailed out to customers to predict women's clothing sales. Please use the SPSS Assignment 8 Lab Book to help guide you through the process of conducting the simple regression, gopyig and pasting the output to a Word document, and using the values provided to write the regression equation for these variables. Results: Identify the primary goai of this analysis. Report the coefcient of determination {rsquared}. Why is this statistic important to this output? Report the F-ratio. Why is this statistic important to this output? Report the tvalue (and E significance) for the slope. Why is this statistic important to this output? Report the regression equation. Refer to the learning guide Chapter 16: Evaluating Regression if you need help answering these questions. Conclusions: Write a brief paragraph about what your results indicate regarding clothing sales for this company. Can you trust the regression line produced from this analysis? Provide evidence to support your answer. Notes Output Created 11-APR-2023 12:54:37 Comments Input Data C:\\Users\\TEMP\\Documents\\Assignment 4 regression.sax Active Dataset DataSet1 Filter Weight Split File N of Rows in Working 120 Data File Missing Value Definition of Missing User-defined missing values are treated Handling as missing. Cases Used Statistics are based on cases with no missing values for any variable used. Syntax REGRESSION /DESCRIPTIVES MEAN STDDEV CORR SIGN /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA /CRITERIA=PIN( 05) POUT(.10) /NOORIGIN /DEPENDENT women /METHOD=ENTER mal. Resources Processor Time 00:00:00.02 Elapsed Time 00:00:00.02 Memory Required 2400 bytes Additional Memory 0 bytes Required for Residual Plots [DataSet1] C:\\Users \\TEMP\\Documents\\Assignment . MarcelomealDescriptive Statistics Mean Std. Deviation Sales of Women's Clothing 40583.6799 12196.43013 120 Number of Catalogs Mailed 10131.77 1697.898 120 Correlations Sales of Women's Number of Clothing Catalogs Mailed Pearson Correlation Sales of Women's Clothing 1.000 681 Number of Catalogs Mailed 681 1.000 Sig. (1-tailed) Sales of Women's Clothing <.001 number of catalogs mailed n sales women clothing variables entered model removed method enter mailed: a. dependent variable: b. all requested entered. summary adjusted r std. error the square estimate .681 predictors: mailedanova sum squares mean f sig. regression residual total coefficients standardized unstandardized b beta .073 .484>