Create a scatterplot and regression output

Assignment Help Applied Statistics

Reference no: EM132660258

Question: Go to the Data and Story Library and click in the Search by Statistical Method box, then click Multiple Regression.

Find a datafile you are interested in that contains at least two quantitative variables appropriate for simple linear regression and download the .TXT file and save to your computer. Choose a dataset that is easy for you to understand and choose TWO quantitative variables that could possibly show a significant relationship.

The following video will show you how to import data from a text file and how to create a scatterplot and regression output in Excel to analyze your data.

Discussion - Correlation and Regression Video Include the answers to the following questions for your chosen datafile and attach your Excel spreadsheet to your response. What is the name of the datafile you chose?

What are the two quantitative variables you chose from the datafile? Which one is your independent variable? Which one is your dependent variable? What is your research question?

The question should be in the format of "Is there a significant correlation between ___ and ___?" Insert your scatterplot (include axis labels) and give the correlation coefficient, r.

Discuss any relationship you see from the scatterplot and r value: Does there appear to be a linear or nonlinear relationship? Is the relationship positive or negative?

Is the linear relationship weak, moderate, or strong? What is the Ho and Ha? What is the name of the test you will run? Insert your Excel regression output in your response using the snipping tool and Insert Image. What is your t-test statistic? What is your p-value? Assuming alpha = .05, what is your decision regarding rejection? What is your conclusion in context?

Give the regression model equation, pick a reasonable hypothetical value for your independent variable, and use the equation to get a prediction for the value of your dependent variable. Based on your previous analysis, do you trust this prediction? Explain.

Reference no: EM132660258

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