Edit: Help me answer these questions using StatCrunch and also insert images from StatCrunch to support your answers.

1. Open the data "Ice cream Sales " on StatCrunch Website.

2. Suppose we want to predict "June Sales" based on "June temperature". Identify, Independent variable: x-variable: Dependent Variable: y-variable: Click on Stat Regression Simple Linear Copy and paste the outputs.

3. Describe the Scatter plot in context (Trend, Strength (check the correlation coefficient), Shape, outliers)

4. Does this positive association mean that high temperatures in June causes high ice cream sales? Explain.

5. Interpret the slope and y-intercept if appropriate. Slope: if ....................................is increased by 1 unit, then on average .............................is predicted to .........................................by ........................................... y-intercept:

6. If June temperature is 68 Fahrenheit predict the Ice cream sales.

7. What is the value of R2 (The variation accounted for by the Model) in the regression line?

8. Interpret the R2 value. ..............% of the variability in sales can be accounted for by variability in temperature. (In other words, ..............% of the variability in ...................... can be accounted for by the linear model.)

9. Based on hypothesis test of regression coefficients, what is your conclusion?