Develop a simple linear regression model using one independent variable to explain the short-term, “risk-free” rate or yield (i.e., 90-day U.S. Treasury bill ) dependent variable. Use monthly data...

72618 assignment
Develop a simple linear regression model using one independent variable to explain the short-term, “risk-free” rate or yield (i.e., 90-day U.S. Treasury bill) dependent variable. Use monthly data over a recent U.S. business cycle (e.g., 2002 to XXXXXXXXXXProvide an overview of the paper. State and justify the null and directional (when wa
anted) alternative hypothesis for the model. Consider selecting one independent variable from among monetary, fiscal, economic, financial, political, demographic and/or another factor you believe relevant. Do not use another interest rate as an independent variable for the first regression model. If your variable increases with time, such as the Consumer Price Index or the Money Supply, change the metric to an annual percent change from the prior year. Is the variable tested significant and consistent with the alternative hypothesis or merely “noise” in the market?
I. Abstract:
The purpose of this paper is to understand the linear relationship between two variables, to find co
elation coefficient, to find out descriptive statistics of two variables, to find regression equation, to check significance of linear relation and to take steps to avoid the limitations of this estimation by way of regression.
Techniques used:
Excel data add ins to find out descriptive statistic
Regression table
Anova
Overall results:
We took dependent variable as 90 days treasury and X as inflation rate and found out regression equation, tested significance using hypothesis test for slopes, p values etc.
II. Key words:
Significance, linear relation, regression, Mean square e
or, std deviation, slope coefficient, predictions, variables.
III. Introduction:
Being asked to find out a regression equation for one variable the variables were selected as 90 days bills for Y and inflation rate (CPI) for X. No of entries were 216 and care was taken to have these independent of each other.
Business cycle used for the years 2002 – 2019 and source was Fred.
Regression Output:
Discussion about linear regression results:
The above table is one we got for finding out linear relationship between independent variable and dependent variable.
Dependent variable y is the 90 days (3 months) treasurybill noted as y
Independent variable is the inflation (Core CPI), noted as x.
The regression analysis was done to find out the relationship (linear) if any between y and x.
IV. Literature review:
The analysis consists of statistical processes for estimating the relationship between y and x. The results have first row R.
R is nothing but the co
elation coefficient and it always lies between -1 and 1. When absolute value of R lies nearer to 1, we say there is a strong linear co
elation, and if it lies near to 0 weak linear co
elation. In our analysis, r = 0.5688, which shows a moderate positive linear co
elation.
The assumptions for linear regression were:
i. The samples were randomly drawn
ii. N , the sample size is sufficiently large.
iii. Observations are independent of each...