• Create histogram of the dependent variable and interpret it in no more than two short sentences in the context of the problem. Do not show the graph.
• Create appropriate scatter plots and describe each of them (including the correlations) in two short sentences. Understanding these graphs is critical to writing down the "correct" model. Do not show the graphs.
• Develop a regression model using the above, run a regression and report your final equation using the estimated values from Stattools. State the p-values for each regression coefficient using the same style as in the word file, Explaining Regression Output posted in this folder.
• In one sentence, describe each of the SLOPE coefficients carefully.
• Explain the R-square for the model in context of the problem.
• Explain the standard error of the regression in context of the problem.
• Examine the residual plot and describe it in one sentence in context of the problem, making sure to address if there is any serious drawback in your model or not.
• There is a 1% chance oil consumption will exceed what value?

Question 3: TX Fuels Company In the last two years, there is a push to switch from natural gas to Bioheat fuels to heat homes, as well as water (https://genesee-energy.comatural-gas-vs-heating-oil/), since new technologies have helped oil-based energy to become clean and sustainable. TX Fuels Company is experimenting with this type of fuel in certain rural areas in Texas. It wants to develop a consumption model for residential customers that depend on oil for their heating/water needs. The data on consumption amounts (in gallons of oil) for 40 customers are given to you in the file TX Fuels.xls. It contains the following variables. The number of degree days. A degree day is equal to the difference between the average daily temperature and 68 degrees F. Hence, if the average temperature is 50, the degree days for that day will equal 18. If the degree days is a negative number, it is recorded as 0. The number of people residing in each home. TX Fuel thinks this might be important in predicting oil usage, given that more people in a home would imply more hot water demand. A third variable is the type of home which is a number between 1 and 5, labeled Home Factor. This value is a composite index representing the home size, age, exposure to wind, insulation, and furnace type. Low index values correspond to lower oil consumption per day. The company would like to use regression methods to estimate and predict oil usage in homes. TX Fuel's management want to entertain variables that are significant at 95% percent confidence. Your task is to help them with this modeling phase. See solution template on what you're specifically asked to do. [Note that in the real world, you will not be given such a template; rather, you will only have access to data and the company's request to use analytics to help them with decision-making.] Question 3 Create histogram of the dependent variable and interpret it in no more than two short sentences in the context of the problem. Do not show the graph. Create appropriate scatter plots and describe each of them (including the correlations) in two short sentences. Understanding these graphs is critical to writing down the \"correct\" model. Do not show the graphs. Develop a regression model using the above, run a regression and report your final equation using the estimated values from Stattools. State the pvalues for each regression coefficient using the same style as in the word file, Explaining Regression Output posted in this folder. In one sentence, describe each of the SLOPE coefficients carefully. Explain the R-square for the model in context of the problem. Explain the standard error of the regression in context of the problem. Examine the residual plot and describe it in one sentence in context of the problem, making sure to address if there is any serious drawback in your model or not. There is a 1% chance oil consumption will exceed what value? Heating Oil at Texas Fuel Gallons # of days Index Count Customer No Oil Usage Degree Days Home Factor Number People 1 381 888 3 3 2 171 176 5 7 3 644 1073 5 4 4 19 126 2 4 5 394 645 5 5 6 153 326 4 6 7 7 1229 1 3 8 319 1218 2 4 9 40 570 2 1 10 121 334 1 7 11 243 738 3 3 12 200 1464 1 5 13 402 880 4 5 14 118 1134 1 5 15 319 1019 3 4 16 185 460 2 3 17 209 257 5 4 18 467 779 5 4 19 50 128 2 4 20 153 371 2 5 21 94 178 3 6 22 574 933 5 3 23 191 295 3 5 24 679 1358 4 5 25 305 626 4 5 26 85 237 2 7 27 87 813 1 6 28 170 385 3 5 29 92 678 1 4 30 35 54 2 3 31 60 314 1 5 32 507 898 4 3 33 148 966 1 6 34 83 84 5 3 35 318 919 3 4 36 85 379 1 4 37 245 512 3 4 38 56 355 2 3 39 303 759 3 3 40 10 777 1 4