Seth Hill
Professor Gwinn
Econometrics
March 3, 2011
Unemployment Rate and Total New Houses Sold

For decades, owning a home has been touted as the very heart of "the American Dream", but today that dream is out of reach for an increasing number of Americans. Why? It is because there are not nearly enough jobs for everyone. Without a jobs recovery, there simply is not going to be a housing recovery. In this report, I will perform a regression analysis to determine the effect of the Unemployment Rate (UR) on Total New Houses Sold (TNHS). I expect that there will be a negative relationship between the two variables. In other words, as the unemployment rate increases, the total number of new houses sold will decrease.

The simple functional form of the model is TNHS=f(UR), where TNHS (measured in thousands) is the dependent variable and UR (16 years and over) is the explanatory variable. To determine the relationship between the two variables, one must set up the Population Regression Function (PRF). The PRF represents the regression line of the population as a whole. The deterministic PRF for the model is E(TNHSt|UR) = B₁ + B₂URt. B1 and B2 are population parameters. B₁ is the intercept coefficient and represents TNHS when UR is zero. In regression analysis, the population regression function is estimated on the basis of the sample regression function (SRF). That is, the PRF is an estimator of the SRF. The deterministic SRF in this case is TNHS = b1 + b2UR. In this function, b1 and b2 are estimators for B1 and B2 in the PRF. The PRF and SRF functions in their stochastic forms are: PRF:TNHSt = B1 + B2URt + Ut

SRFTNHSt = b1 + b2URt + et
In the PRF, Ut is the population error term. The population error term is a random variable that cannot be explained by the PRF. This term represents the difference between the actual value of TNHS and the value predicted by the regression equation. In other words, the error term accounts for variables that affect TNHS...

...the number of construction permits issued at present.
Example 2: The demand for new house or automobile is very much affected by the interest rates changed by banks.
Regressionanalysis is one such causal method. It is not limited to locating the straight line of best fit.
Types:-
1. Simple (or Bivariate) RegressionAnalysis:
Deals with a Single independent variable that determines the value of a dependent...

...Answers to Midterm Test No. 1
1. Consider a regression model of relating Y (the dependent variable) to X (the independent
variable) Yi = (0 + (1Xi+ (i where (i is the stochastic or error term. Suppose that the
estimated regression equation is stated as Yi = (0 + (1Xi and ei is the residual error term.
A. What is ei and define it precisely. Explain how it is related to (i.
ei is the residual error term in the sample...

...you
cannot consult the regression R2 because
(a) ln(Y) may be negative for 0 < Y < 1.
(b) the TSS are not measured in the same units between the two models.
(c) the slope no longer indicates the effect of a unit change of X on Y in the log-linear
model.
(d) the regression R2 can be greater than one in the second model.
1
(v) The exponential function
(a) is the inverse of the natural logarithm function.
(b) does not play an important role in modeling nonlinear...

...RegressionAnalysis Exercises
1- A farmer wanted to find the relationship between the amount of fertilizer used and the yield of corn. He selected seven acres of his land on which he used different amounts of fertilizer to grow corn. The following table gives the amount (in pounds) of fertilizer used and the yield (in bushels) of corn for each of the seven acres.
|Fertilizer Used |Yield of Corn...

...The simpleregression model (SRM) is model for association in the population between an explanatory variable X and response Y. The SRM states that these averages align on a line with intercept β0 and slope β1: µy|x = E(Y|X = x) = β0 + β1x
Deviation from the Mean
The deviation of observed responses around the conditional means µy|x are called errors (ε). The error’s equation: ε = y - µy|x
Errors can be positive or negative, depending on whether data lie above...

...RegressionAnalysis (Tom’s Used Mustangs)
Irving Campus
GM 533: Applied Managerial Statistics
04/19/2012
Memo
To:
From:
Date: April 19st, 2012
Re: Statistic Analysis on price settings
Various hypothesis tests were compared as well as several multiple regressions in order to identify the factors that would manipulate the selling price of Ford Mustangs. The data being used contains observations on 35 used Mustangs...

...
Mortality Rates
RegressionAnalysis of Multiple Variables
Neil Bhatt
993569302
Sta 108 P. Burman
11 total pages
The question being posed in this experiment is to understand whether or not pollution has an impact on the mortality rate. Taking data from 60 cities (n=60) where the responsive variable Y = mortality rate per population of 100,000, whose variables include Education, Percent of the population that is...

...REGRESSIONANALYSIS
Correlation only indicates the degree and direction of relationship between two variables. It does not, necessarily connote a cause-effect relationship. Even when there are grounds to believe the causal relationship exits, correlation does not tell us which variable is the cause and which, the effect. For example, the demand for a commodity and its price will generally be found to be correlated, but the question whether demand depends on...