Things to Review:
Section A: Wording of Business Question
Section B: X Dependent Variable not stated or the type of data
Section D 1: Null Wording
Section D2: P-number value is not evident
Section D4: Do not understand the use of the word drilling please change this section.
Note that linear regression analysis addresses questions about significant relationships but the null hypothesis states “no significant” relationship.
A – QUESTION: Did you state a question about significant relationship or significant prediction for X and Y? The stated question wording cannot be causative…for example, “influence” or “increases/decreases” or “impact” which imply we know the direction prior to analysis of the data. Regression cannot address causation. Regression also does not address difference.
B1/B2 – DATA/DISPLAY: Is participation rate the X Independent variable? Is it shown on the X axis of the Line Fit Plot (the scatter plot with equation shown)? Is the quantity of data equal to the number of observations? Type of data is same as level of measurement. Is the data type Nominal, Ordinal, Interval, or Ratio? See Module 1.08 in the MindEdge textbook and note that you can have zero of these variables.
o D1 – NULL: You must state the null hypothesis in words BEFORE you can accept/reject based on p-value. Then, the appropriate p-value number must be stated and compared to 0.05 which will inform whether to accept or reject the null. Note that linear regression analysis addresses questions about significant relationships (relationships and not causation, not difference!), but the null hypothesis states “no significant” relationship.
o D2A – R-SQUARE: We must compare R-square based on the 0 to 1 range for Goodness of Fit. Once we state the range, and we know our value, we can then determine where our R-square fits (weak, moderate, strong fit?).
D2B – SIGNIFICANCE: What does an E for exponent mean if on the p-value? The appropriate p-value number must be stated and then compared to 0.05. Do you compare the appropriate p-value to 0.05? You must use ONLY the appropriate p-value to establish competency (see the image below). Do you accept or reject the Null based on your p-value?
D1 – NULL: You must state the null hypothesis in words BEFORE you can accept/reject based on p-value. Then, the appropriate p-value number must be stated and compared to 0.05 which will inform whether to accept or reject the null. Note that linear regression analysis addresses questions about significant relationships (relationships and not causation!), but the null hypothesis states “no significant” relationship.
o D2c – LINEAR EQUATION: Did you follow the course video to generate the correct equation on your plot? Did you accurately state the linear equation in your task report, including any negative or positive sign? What is the linear equation used for in the future? Why can we use our result (considering R-square for reliability and p-value for significance)?
The recommended course of action should tie directly to the X and Y variables.
