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# Calculations with Multiple Regressions

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Your study includes measuring several physical characteristics, including weight of the communication device in grams (TW), size in centimetres (SZ), the size of the screen in percentage (%SC), and estimated battery life in months. The following table shows summary statistics for these variables.

The following table shows the results of fitting a multiple regression model for predicting the weight using the other variables. The predictive power is good with R² = 0.669.

QUESTIONS
a. Interpret the effect of battery life on weight in the multiple regression equation.
b. In the population, does battery life help you predict weight if you already know the size and percentage of size of screen?

#### Solution Preview

a. Interpret the effect of battery life on weight in the multiple regression equation.
NOTE: can you please check the second table (predictors)? I think TW should be SZ.
First, the multiple regression life is ...

#### Solution Summary

This solution discusses the impact of each of the independent variable on the dependent variable.

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## Multiple Regression Equation Development in SPSS

Develop a hypothetical multiple regression (prediction) equation to predict something in your area of professional or personal interest.

a. First, identify the dependent (criterion) variable that you are interested in predicting. What variable do you plan to predict? Next, choose two variables (called independent) that you will use to predict your chosen dependent criterion variable.

b. Make-up at least 10 values for y, x1, and x2.

c. Calculate two correlation coefficients in SPSS.

d. Using your dependent criterion variable as "y," and your predictor independent variables as x1and x2, use SPSS to create the multiple regression prediction equation based on your table of 10 (or more) values for y, x1, and x2.

e. Use values for x1 and x2 to predict y using the equation you created in Step D.

Complete detailed instructions attached.

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