Ways to Overcome the Autocorrelation Problem

Several approaches to data analysis can be used when autocorrelation is present. One uses additional independent variables and another transforms the independent variable. •Addition of Independent Variables Often the reason autocorrelation occurs in regression analyses is that one or more important predictor variables have been left out of the analysis. For example, suppose a researcher develops a regression forecasting model that attempts to predict sales of new homes by sales of used homes over some period of time. Such a model might contain significant autocorrelation. The exclusion of the variable “prime mortgage interest rate” might be a factor driving the autocorrelation between the other two variables. Adding this variable to the regression model might significantly reduce the autocorrelation. •Transforming Variables…

Multicollinearity

One problem that can arise in multiple regression analysis is multicollinearity. Multicollinearity is when two or more of the independent variables of a multiple regression model are highly correlated. Technically, if two of the independent variables are correlated, we have collinearity; when three or more independent variables are correlated, we have multicollinearity. However, the two terms are frequently used interchangeably. The reality of business research is that most of the time some correlation between predictors (independent variables) will be present. The problem of multicollinearity arises when the inter-correlation between predictor variables is high. This relationship causes several other problems, particularly in the interpretation of the analysis. 1.It is difficult, if not impossible, to interpret the estimates of the regression coefficients….

Exploratory Data Analysis

Exploratory Data Analysis Using the dataset Chamorro-Premuzic. sav, exploratory statistical analysis was carried out on the variables in the dataset. Scatter plots were formulated t give a clear visual view of the data for Extroversion and Agreeableness. Descriptive statistics were also formulated for the variables. 2. Decision about the missing data 3. Correlation A correlation analysis was carried out on the data for Extroversion and Agreeableness.

Personal consumption expenditures price index

In their volume Consumer Demand in the United States: Analyses and Projection (Cambridge, Mass: Harvard University Press, 1970), p. 119, H. S. Houthakker and L. D Taylor presented the following results for their estimated demand equation for local bus service over the period from 1929 to 1961 (excluding the 1942 through 1945 war years) in the United States. Qt = 22. 819 + 0. 0159 Xt – 0. 1156 Pt – 86. 106 St – 0. 9841 Dt Qt = per capita personal consumption expenditures on bus transportation during year t. Xt = total per capita consumption expenditure during year t. Pt = relative price of bus transportation in year t. St = car stock per capita in year t….

Academic performance

Purpose of project Over the years at Queen’s Royal College I have seen teachers having stern conversations with students for reaching to school late habitually. These students are faced with consequences such as: “in-house suspension” or community service for regular late coming. I myself have been a victim of these punishments. It is believed that students who are frequently late are indiscipline, and this can spill over into their study habits, hence affecting their overall performance in their internal examinations. On the other hand, some share different views that punctuality has no effect on a student’s performance. Reason being, students do extra studies at home, hence making up for lost time at school. In that context I would like to…