if you could help me with a summary include the findings I have below. that would be so great to help me put what I did in the writing form ?English isn't my main language

question from professor:Summarize your results from Steps 1-11 in a report. The report should explain and interpret the results in ways that are understandable to someone who does not know statistics.

Here is the work I have done with the questions and answers belowfor you to use.

Step 1 scatterplot: For the dependent variable I used (Y "Sales") and the selected independent variable I chose (X2 "Time").

Step 2 : Determine the equation of the "best fit" linebetween the independent X (time) and dependent Y (sale ) variables

y = 48.25421757 - 0.219344686 * x

Step 3 Determine the correlation coefficient. Interpret

r^2= - 0.133987857

This correlation coefficient is very close to 0, therefore we can conclude that there is a weak negative relationship between the two variables.

Step 4.Determine the coefficient of determination. Interpret.

coefficient of determination = 0.017952746 or 1.795%

which indicates that 1.795% of the variation in sales can be described or explained by the time and

Step 5Test the utility of this regression model by completing a hypothesis test of b=0 using =0.10. Interpret results, including the p-value.

The T test for the slope is given in the regression output as:-1.3385

The P value for a 2 sample alternative at a of 98 level is 0.1838

since the P value is greater than the significance level of 0.10, we can conclude that we will fail to reject the null hypothesis and that the slope = 0

Step 6Based on the findings in steps 1-5, analyze the ability of the independent variable to predict the dependent variable.

Based off the variables I chose there is not a good enough to predict the dependent variable there is not a very good correlation.

Step 7 Compute the confidence interval for b, using a 95% confidence level. Interpret this interval.

At the 95% confidence interval the slope that was found is given in the regression output as ( -0.544550882 to 0.105861509 )

My interpretation is we can be 95% confident that the actual value of the slope of regression between time and sales is between -0.54455 and 0.10586

Step 8Compute the 99% confidence interval for the dependent variable, for a selected value of the independent variable. Each student can choose a value to use for the independent variable (use same value in the next step). Interpret this interval.

The 99% confidence interval for the sales when the time =14

Error= 0.0619

T value 99% confidence interval with a degree of 98 = 2.6269

Margin of error =0.1626

45.18340.1626= (45.0208,45.346)

Confidence interval =45.0208

we can be 99% confident that the actual number of sales when time is =14 is between 45.0208 and 45.346

Step 9Using the same chosen value for part (8), estimate the 99% prediction interval for the dependent variable. Interpret this interval.

Error=0.5748

T value for 99% confidence interval at a 98 degree= 2.6269

Margin of error = 1.5099

45.18341.5099= (43.6735,46.6933)

Prediction interval =43.6735<y<46.6933

My interpretation is that we are 99% sure that the next prediction sales for a time of 14 will be within this interval.

Step 10 What can be said about the value of the dependent variable for values of the independent variable that are outside the range of the sample values? Explain.

This relationship is not very strong. We shouldn't use the time variable to predict the value of the dependent variables for independent variable values which are also outside the sample values.

Step 11 Describe a business decision that could be made based on the results of this analysis. In other words, how might the business operations change based on these statistical results.

Based off the analysis I can conclude that because time doesn't affect sales that weather we increase or decrease the time it would not have a direct effect on the sales.