1. The Following data is given for the period 1999-2003InterestrateInflationiTT19994.74.420004.65.420016.35.720024.84.620032.92.4a. According to Fisher Equation “a rise in inflation also rises the nominal interest rate by thesame amount". Construct a regression model by determining dependent and independentvariables according to this theory.b. Obtain residuals of the regression. Calculate variance of the residuals and the standard errorsof the parameters (note: use the table above for calculations, just write down the final resultshere).c. Test significance of the slope parameter (the parameter of the independent variable) at 5%significance level. Is it in line with the proposition?d. Evaluate the overall significance of the model with R² and F test||

The data is given below : i π 1999 4.7 4.4 2000 4.6 5.4 2001 6.3 5.7 2002 4.8 4.6…

Answered: 1. The Following data is given for the…

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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Relax mortgage has gathered following data to examine the relationship between housing starts and mortgage interest rate. You are required to;i. Derive the regression equation ii. Estimate the no of housing starts if mortgage interest rate is 2.5% iii. Calculate and interpret the correlation between interest rate and no ofhousing starts.
Below you are given information on Federal Bank’s Net Income for the past 7 years. Year Income (Millions) 1 15.0 2 16.2 3 17.1 4 18.1 5 18.8 6 19.2 7 20.5 Use regression analysis to obtain an expression for the linear trend component. Forecast the Bank’s Net income for the next 5 years. NB: We are using the Simple Linear Regression equation which is Y = a + bX
Historical demand for a product is as follows: DEMAND April 60 May 55 June 75 July 60 August 80 September 75 Using a simple four-month moving average, calculate a forecast for October. Note: Round your answer to 1 decimal place. Using single exponential smoothing with α = 0.2 and a September forecast = 65, calculate a forecast for October. Note: Round your answer to the nearest whole number. Using simple linear regression, calculate the trend line for the historical data. Say the X axis is April = 1, May = 2, and so on, while the Y axis is demand. Note: Round your intercept value to the nearest whole number and slope value to 2 decimal places. Calculate a forecast for October using your regression formula. Note: Round your answer to 2 decimal places.
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Suppose a new location opens in an area with a population of 144,000, an average income of$36,000, an average age of 27, and $2,000 spent on advertising in the previous month.a. Use your chosen regression to predict Gross Sales for the month at the new location. (I picked population)b. Suppose actual Gross Sales for the month were $420,624. Does this make sense,given your model and predicted value?12. Using your chosen regression model,a. Identify the slope and explain what it means, in the context of the model.b. Identify the initial value or y-intercept and explain what it means, in the context ofthe model.
Relax mortgage has gathered following data to examine the relationship between housing starts and mortgage interest rate.Interest rate3.53.02.83.62.753.43.122.863.022.63.3Housing starts10012015013017013513018512719096You are required to;i. Derive the regression equation ii. Estimate the no of housing starts if mortgage interest rate is 2.5% iii. Calculate and interpret the correlation between interest rate and no of housing starts.
Interest rate Inflation i π 1999 4.7 4.4 2000 4.6 5.4 2001 6.3 5.7 2002 4.8 4.6 2003 2.9 2.4 a. According to Fisher Equation “a rise in inflation also rises the nominal interest rate by the same amount”. Construct a regression model by determining dependent and independent variables according to this theory.
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Refer to the data below which contains the percentage change (X) in a stock market index over the first five trading days of the year and percentage change (Y) in the index over the whole year. Complete parts a and b. (X) (Y) 1.4 0.2 14.7 - 9.4 -0.3 2.9 19.6 20.2 a. Estimate the linear regression of Y on X. A y₁ = taxi (Round to four decimal places as needed.) 2.2 - 3.8 - 1.9 27.7 - 1.3 5.7 22.6 2.4 -1.5 1.3 11.8 26 1.5 - 4.4 - 4.4 20.5 0.7 - 1.0
The table shows the percents x and numbers y (in millions) of women in the work force for selected years. T 1970 1975 1980 1985 1990 1995 2000 2005 Percent, x 43.3 46.3 51.5 54.5 57.5 58.9 59.9 59.3 Number, y 31.5 37.5 45.5 51.1 56.8 60.9 66.3 69.3 Year (a) Use the regression capabilities of a graphing utility to find the least squares regression line for the data. (Round your coefficients to two decimal places) YM (b) According to this model, approximately how many women enter the labor force for each one-point increase in the percent of women in the labor force? (Round your answer to two decimal places.) million women
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Consider the following OLS regression results, In(inc)=1.970+.083educ, R²=.186, where inc represents annual income (in $1000s) and educ represents years of education. The slope estimate on years of education can be interpreted as an additional year of education is associated with an .083% increase in income. an additional year of education is associated with an 8.3% increase in income. a 1% increase in education is associated with an increase in income of $83. a 1% increase in education is associated with an increase in income of $8,300.

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