BUS2023-N02 Business Statistics - Regression Assignment
In your explanation you must focus on the following:
-The steps provided in the PowerPoint presentations and in the accompanying Excel worksheets.
-Statistical reasons and logic for why you selected the independent variables you selected. For each independent variable you must do the following:
- Be sure to explain why it would be appropriate to test or why it would be appropriate to leave out of the tests, the independent variables included in the data. Explain how each independent variable logically would impact the dependent variable or would not logically impact the dependent variable.
- Are your independent variables truly independent? Is the proposed "Independent Variable" dependent on the "Dependent Variable"?
- Will the independent variable help determine future quantities of electricity purchases?
- Does this independent variable help explain the reason residential customers use more or less electricity?
- Is your logical explanation supported by the statistical results that you get? Demonstrate that you can support your answer.
-Show your scatter diagram between each independent variable and the dependent variable.
-The correlations between the dependent variable and the independent variables.
-The correlation between independent variables to include an explanation of potential multicollinearity.
-What inferences do you gain by looking at the coefficients of the independent variables?
-How does the adjusted r2 and the standard error of the equation impact selecting a good equation?
-What significance level did you use? Explain.
-Explain the meaning of each of the independent variable's t-statistic.
-In the approach used in this class, the ANOVA table results are optional, i.e. not required.
When I say OPTIONAL, I mean optional. If you choose to use the ANOVA table results in your discussion of your results, that is your choice; however, you had better be quite knowledgeable in the use of the values or this will cause your grade to be lowered.
-Follow the instructions below as to where to post your results.
Without a full explanation of the points above, your paper will not be deemed to meet the requirements of the assignment. You must use Microsoft Excel for all statistical analysis. Other software packages are not acceptable.
Part 1 is to develop an equation that best models the relationship between the independent variable, Y, and one or more of the dependent variables, X1, X2, X3,..., Xn, using multiple linear regression techniques. The monthly data found in the MS Excel file named Regression Assignment Data - 2016 is to be used in your assignment. The dependent variable, Y, is OK Residential MWH, described in number 1, below, and the potential independent variables, X1, X2, X3,...,Xn, are described as numbers 2 through 12, below. [Hint: Not all the provided independent variables should be used in the final equation. You must determine which independent variables model the dependent variable the best. You should eliminate independent variables that cause multicollinearity.
Part 2 - Discussion: You must describe the process and logic used for selecting the particular independent variables that you selected for your final equation. In your description, you must explain the process of testing different independent variables and the results of various tests, plus describe the summary statistics that helped you select the best independent variables. Be sure to explain why you excluded some of the potential independent variables. When you post Part 2 in the Discussion Forum, you are sharing with your classmates what you learned from your work. This is a good thing. Even though this is a small part of the total points, it is a required part of the assignment. If Part 2 is not completed, Part 3 cannot earn the maximum grade
Part 3 is worth 60 points. This is the most important section. Based upon input from other class members, refine your regression model and your explanation. Included in your final submitted files, Excel and Word files, should be all the requirements of Part 1 and Part 2, plus any improvements you make after reviewing Part 2 of other students. Further, you must develop a forecast of the monthly KWH sales for the year 2016, using the data in MWH Regression Data in lines 219 - 230. If you have a creative idea how to present your final model and forecast, as long as you meet the requirements of explaining what you did to build your model and demonstrate your forecast, use your creative idea.
Description of Independent and Dependent Variables:
In the MS Excel file named Regression Assignment Data, you will find the following data series with the labels as follows:
1. OK Residential MWH - Oklahoma residential sales of electricity in thousands of KWH (1000 KWH = Megawatt Hours- MWH). Special Instructions: This is your dependent variable in this assignment.
2. OK Residential Electric Price ($/KWH) - average Oklahoma Residential Electric Price (Dollars per KWH) for each month. Special Instructions: You will include this independent variable regardless of the correlations you find or the t-stat. It is a required independent variable.
Potential Independent variables are listed below. You must determine which should be included and which should be excluded:
3. OK Real Personal Income (2000 $Mil) - Oklahoma Real Personal Income in millions of constant 2000 dollars. Personal income is the income earned by persons in the state. It does not include income earned by businesses.
4. OK Real Non-Farm Real Personal Income ($ Mil 2000) - Oklahoma real personal income excluding income earned by farming proprietors in 2000 dollars.
5. OK Total Spending ($ Mil 2000) - Oklahoma monthly (non annualized) real total spending on all consumer goods in millions of constant 2000 dollars. This includes expenditures on all consumer goods such as food, clothing, housing, entertainment, utilities purchases which includes gas usage and billing, electric usage and billing, water usage and billing, and plus sewer services, telecommunications, etc. Electric sales would be a significant portion of this value.
6. OK Real GSP (2000 $Mil) - Oklahoma real gross state product (GSP) in millions of constant 2000 dollars. GSP is a measure of output of goods and services in the state; electric output would be part of this figure. Output is closely tied to total income.
7. Oklahoma Population (Thou) - Monthly Oklahoma population in thousands.
8. Oklahoma Non-Farm Population (Thou) - Monthly non-rural Oklahoma population in thousands.
9. OK Winter HDD - Oklahoma heating degree days - this is a numerical value representing hours in the month where heating is required. The larger the number the more a residential customer might demand for heating purposes.
10. OK Summer CDD - Oklahoma cooling degree days - this is a numerical value representing hours in the month where cooling is required. The larger the number the more a residential customer might demand use of their air conditioners.
Attachment:- Regression Assignment Data.rar