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Construct your initial multivariate model by selecting a dependent variable Y and two independent variables X. Clearly define what each variable represents and how this relates to the overall theme you've been assigned. Explain the reasons for constructing this model by discussing the expected relationship between the Y and each X variable.
Step Simple Regression Analysis
Complete a separate simple regression analysis for Y and each X that you have selected for the model. Provide the equation, interpret the slope, and state its significance.. Also discuss the overall model performance for each X variable by looking at the adjusted R square. You should also plot the relationship between Y and each X in your model. Use the results of this step to comment on whether the chosen independent variables are suitable for inclusion in a multiple regression analysis. If you determine that an X variable is not appropriate, you should replace it with another variable.
Each question, by default, should be solved INDIVIDUALLY, unless marked as \collaborative". Questions marked as \collaborative" implies that for those questions you are encourage
why we use dummy variable
Related Positional Measures Besides median, there are other measures which divide a series into equal parts. Important amongst these are quartiles, deciles and percentiles.
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Sampling Error It is the difference between the value of the actual population parameter and the sample statistic. Samples are used to arrive at conclusions regarding the p
Stratified Sampling Stratified Sampling is generally used when the population is heterogeneous. In this case, the population is first subdivided into several parts (or s
1. Recognize and explain the opportunities for statistical learning. 2. Describe how the use of statistics supports student learning. 3. Recognize appropriate data displays a
data:59,59,65,70,74 176,179,195,210,200
Try different numbers of clusters in your program (K=2...15) and build a plot that shows the dependency between number K and value of RSS function on the last iteration. What is th
As one of the oldest multivariate statistical methods of data reduction, Principal Component Analysis (PCA)simplifies a dataset by producing a small number of derived
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