Answer the following questions using the data given below. moviegoer spending on snacks (n = 10) age (x) ($) spent (y) 30 6.85 50 10.50 34 5.50 12 10.35 37 10.20 33 10.75 36 7.60 26 10.10 18 12.35 46 8.35 use excel to make a scatter plot of the data. select the data points, right-click, select add trendline, select the options tab, and choose display equation on chart and display r-squared value on chart.

y=-0.053 X +10.961

R^2=0.0851

Step-by-step explanation:

1) Important concepts

Linear models "describe a continuous response variable as a function of one or more predictor variables. They can help to understand and predict the behavior of complex systems".

Regression analysis is a powerful statistical method that allows you to examine the relationship between two or more variables of interest.

R-squared (R^2) is "a statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model".

2) Solution to the problem

The table with the data is below:

X(Age)Y($Spent)

30          6.85

50          10.5

34          5.5

12          10.35

37          10.2

33          10.75

36             7.6

26          10.1

18          12.35

46          8.35

We do the following procedure

1. We select the data

2. Click on Insert

3. Then click on Insert Scatter (X,Y) or Bubble Chart

4. Then we select the option Scatter

5. A dispersion graph would be show up

6. Then we click on Quick Layout

7. And we select the Layout 9

8. We remove the legend on the right

9. Double click on the axis and edit the names. For the X axis is Age and for the Y axis would be Amount Spending on Snacks.

10. Double click on The title and edit the title as Amount spending on snacks vs Age

11. Double click on the y axis

12. Click on axis options

13. For the minimum value select 4 and for the maximum 14

14. Double click on the x axis

15. Click on axis options

16. For the minimum select 10 and for the maximum 50

And our graph would be done. The output is attached.

The equation obtained was:

y=-0.053 X +10.961

That means for every additional year the amount spending on snacks decrease about 0.053$.

R^2=0.0851

The R square values is very low and that means that we have a low % of the variance explained by the linear model. So probably the linear model is not the best for this dataset.

1^3 + 2^3 + 3^3 +4^3 = (1+2+3+4)^2

step-by-step explanation:

i am only able to answer the first part. i am not sure about the second part. sorry