For the project below, I was given a set of data. But I'm told to analyze it in 4 different ways. I am unsure how to perform the underlined version with the data I was given. I was thinking of using Regression model in data analysis in excel, but it doesn't give me all of the data I'm looking for. Could you help me to understand how to analyze the data in the way the project requires?

Quantitative Project

Project Description:

The purpose of this project is two-fold. The first is to outline the relationship between personal consumption and disposable income. The second is to provide a simple forecast of personal consumption.

Each data set provided includes quarterly data going from the second quarter of 1981 (1981:2) to the first quarter of 2021 (2021:1). The variables included are personal consumption (PCONS), disposable personal income (DPI) and trend (TREND). For the first portion of this project, you need to discuss the relationship between personal consumption and disposable income. You will want to include a discussion surrounding a simple linear regression and one that also includes trend. You will also want to consider an additional model that addresses any nonlinearity in DPI that might be useful. Only use the square, square root, reciprocal, and natural logarithm as the potential nonlinear transformations. Do not transform trend in this analysis but you may still include that variable, if you wish, in any of your work.

The second part of this project is to provide a simple forecast of personal consumption (PCONS) using the simple and quadratic trend models. You want to forecast PCONS for the next two quarters (2021:2 and 2021:3) so there is only a forecast horizon of two.

A B C D G H 1 Quantitative Project Data 2 Spring 2021 4 PCONS = Personal Consumption Expenditures (Seasonally Adjusted, $ billions) 5 DPI = Disposable Personal Income (Seasonally Adjusted, $ billions) 6 TREND = Trend or Observation 7 8 g 10 Year Quarter PCONS DPI TREND 11 1981 2 1831.874 2125.848 H 12 1981 3 1885.734 2177.992 2 13 1981 4 1917.524 2217.133 3 14 1982 1 1958.099 2302.221 15 1982 2 1974.447 2339.999 16 1982 3 2014.155 2375.323 6 17 1982 4 2039.645 2412.547 7 18 1983 1 2085.671 2462.319 19 1983 2 2145.554 2497.228 9 20 1983 3 2184.589 2541.526 10 21 1983 4 2249.438 2582.521 11 22 1984 1 2319.895 2655.754 12 23 1984 2 2372.496 2732.912 13 24 1984 3 2418.165 2817.775 14 25 1984 4 2475.876 2892.421 15 26 1985 1 2513.523 2953.265 16 27 1985 2 2561.797 2995.748 17 28 1985 3 2636.008 3023.121 18 29 1985 4 2681.764 3106.975 19 30 1986 1 2754.148 3121.548 20 31 1986 2 2779.400 3176.623 21 32 1986 3 2823.648 3241.309 22 33 1986 2851.456 3279.358 23 34 1987 2917.201 3317.667 24 35 1987 2 2952.807 3342.776 25 Sheet1