The Ordinary Least Squares Technique

The estimated total volume change is shown in Figure 1. Because the slope of the data trend after

The R2 value determines how close the data is to the function (Penn State). The

results from the model were both accurate and reliable. The multiple R and the R

* Correlation coefficient (R-squared) – This represents how well the independent variables (X) explain the response variable (Y).

4) Use exponential regression (or find a constant ratio) to determine an equation for the data.

Referring to Figure 6.2.3 and 6.3.3, it was proven that our model has problems that the sample data used does not represent the whole population. Therefore, this is one of the flaws in our research. A more constructive suggestion to eliminate this problem would be to extend the research with a larger sample size with longer time horizon. And if the sample size is large enough, the time series issue can be neglected.

It is possible for a measure to have one and not all of the above criteria’s. If it lacks one of the measurements the chances of the study/research lacking credibility are higher.

29. A distribution center for a chain of electronics supply stores fills and ships orders to retail outlets. A random sample of orders is selected as they are received and the dollar amount of the order (in thousands of dollars) is recorded, and then the time (in hours) required to fill the order and have it ready for shipping is determined. A scatterplot showing the times as the response variable and the dollar amounts (in thousands of dollars) as the predictor shows a linear trend. The least squares regression line is determined to be: yˆ= 0.76 +1.8x. A plot of the residuals versus the dollar amounts showed no pattern, and the following values were reported: Correlation r +0.90; R 2 = 0.81; standard deviation of the residuals is 0.48. What percentage of the variation in the times required to prepare an order for shipping is accounted for by the fitted line?

10. Refer to the model and estimates in the previous question. You want to test that

The slant of the relationship is characterized as the measure of progress in one variable given a unit change in the other variable. In the given relationship, the incline is given by the measure of fall in cost required to expand number of visits by a unit.

2. The thing that ended up taking away that .5 point on my post test is that I made a mistake on my wording for the average rate of change explanation. I need to review my notes a lot about how the proper structure of the average rate of change sentence is supposed to be.

Explanation: The difference in the amount of point’s between7% and 9% is 200 basis points. Due to the fact that 9%=900 and 7%=700 so the difference amount is 200 basis points.

The absolute value of intercept (alpha) in the Three-Factor Model for Low Portfolio is 1.1159, 0.1347 for 5 Portfolio, 0.6354 for High Portfolio, and 1.4591 for High-Minus-Low Portfolio. They all are larger than those in the Four-Factor Model, given 0.2081 for Low Portfolio, 0.0961 for 5 Portfolio, 0.0412 for High Portfolio, and 0.0402 for High-Minus-Low Portfolio. Therefore, alpha, a measurement of deviation from the model, is much smaller in the Four-Factor Model than in the Three-Factor Model. It shows that the Four-Factor Model fits the real situation better. In addition, we could see that R-Square (percentage of data that can be explained by the model) for the

The procedure for this model is to collect several periods of history relating to the independent and dependent variables themselves, establish the relationship that minimizes mean squared error of forecast vs actual using linear or non-linear and singular or multiple regression analysis.

Run the regression Report your answer in the format of equation 5.8 (Chapter 5, p. 152) in the textbook including and the standard error of the regression (SER). Interpret the estimated slope parameter for LOT. In the interpretation, please note that PRICE is measured in thousands of dollars and LOT is measured in acres.