Regression analysis and credit balance

The sketch below illustrates an example of a linear regression line drawn through a series of X, Y observations: Linear Regression A linear regression line is usually determined quantitatively by a best-fit procedure such as least squares i. In linear regression, one variable is plotted on the X axis and the other on the Y. The X variable is said to be the independent variable, and the Y is said to be the dependent variable.

Regression analysis and credit balance

Regression analysis and credit balance

Determine the coefficient of correlation. Determine the coefficient of determination. Interpret your results, including the p-value.

Regression analysis and credit balance

State the equation for this multiple regression model. Perform the t-test on each independent variable. Explain your conclusions and clearly state how you should proceed. In particular, which independent variables should we keep and which should be discarded.

If appropriate, re-run the multiple regression using only the significant independent variables and the dependent variable. Provide your output and interpret it. Is this multiple regression model better than the linear model that we generated in parts ? Explain Project Part B: Hypothesis Testing and Confidence Intervals Your manager has speculated the following: Also be sure to compute the p-value and interpret.

Write a report to your manager about the results, distilling down the results in a way that would be understandable to someone who does not know statistics.

Regression and Correlation Analysis | Essay Example

Clear explanations and interpretations are critical. AJ DAVIS is a department store chain, which has many credit customers and wants to find out more information about these customers. A sample of 50 credit customers is selected with data collected on the following five variables: For each of the three Parts of your Course Project, you will load the data set into Minitab.

For each of the five variables, process, organize, present and summarize the data. Analyze each variable by itself using graphical and numerical techniques of summarization. You may wish to use some of the following graphs: More is not necessarily better.

In addition be sure to find the appropriate measures of central tendency, and measures of dispersion for the above data. Analyze the connections or relationships between the variables.

Use graphical as well as numerical summary measures. Explain what you see. Be sure to consider all 10 pairings. Some variables show clear relationships, while others do not.

Prepare your report in Microsoft Word or some other word processing packageintegrating your graphs and tables with text explanations and interpretations. Be sure that you have graphical and numerical back up for your explanations and interpretations.

Be selective in what you include in the report. Rather what I want you do is to highlight what you see for three individual variables no more than 1 graph for each, one or two measures of central tendency and variability as appropriateand two or three sentences of interpretation.

For the 10 pairings, identify and report only on three of the pairings, again using graphical and numerical summary as appropriatewith interpretations.


Please note that at least one of your pairings must include Location and at least one of your pairings must not include Location. TCO A Seventeen salespeople reported the following number of sales calls completed last month. In a sample of 20 customers, find the probability that 4.

TCO B The demand for gasoline at a local service station is normally distributed with a mean of 27, gallons per day and a standard deviation of 4, gallons per day 5.

Using MINITAB, Perform the Regression and Correlation Analysis

TCO C An operations analyst from an airline company has been asked to develop a fairly accurate estimate of the mean refueling and baggage handling time at a foreign airport.Jun 19,  · Project Part C: Regression and Correlation Analysis Back to Top Using MINITAB perform the regression and correlation analysis for the data on CREDIT BALANCE (Y) and SIZE (X) by 5/5.

Correct. we cannot use the given regression model to accurately estimate the credit balance for a customer that has a household size of The household size of 5 average credit balances for customers is estimated to lie within the interval of ( 95% CI ( 95% CI ( predict the credit balance for a customer.

The most popular use of regression analysis is on investment returns, where the market index is independent while the individual security or mutual fund is dependent on the market. What can we say about the credit balance for a customer that has a household size of 10?

Denotes a point that is an extreme outlier in the predictors.

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Using MINITAB run the multiple regression analysis using the variables INCOME, SIZE and YEARS to predict CREDIT BALANCE. Using EXCEL perform the regression and correlation analysis for the data on CREDIT BALANCE (Y) and SIZE (X) by answering the following.

1. Generate a scatterplot for CREDIT BALANCE vs. SIZE, including the graph of the "best fit" line. Linear regression for the advertising data Consider the advertising data shown on the next slide.

Questions we might ask: Is there a relationship between advertising budget and.

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