best clear cut understanding of linear regression. Thanks you Sir. I would really need to know about power, expo and polynomial algorithm. Please provide any link for it too.
Thanks for the captivating video! 👍👍👍👍👍👍 As a Statistical Data Scientist; I would have proceeded as the following: 1. ☄ Create the Frequency Distibution of the Dependent Variable (i.e. Price= DV). To do so; You can select "Price" measure, Click on "Show Me", & select "Histogram". Clearly; "Price"'s distribution will be SKEWED. So; It is preferable to use "MEDIAN Price" in lieu of "Sum Price". 2. ☄Using Average Number of Rooms (i.e. RM) does NOT make any sense. In fact; "RM" should be a positive integer number (1 room, 2 rooms, 3 rooms,.. .). For instance; RM= 6.575 is practically senseless. 3. ☄ Assumptions should be verified. Mathematically speaking; Residuals ~ Normal Distribution (mean=0, Constant Variance). 4. ☄ Testing the following Hypothesis: H0: No correlation between DV & IV [r ( Price, RM)= 0) Vs. Ha: Correlation 5. ☄ Since computed P-value is way less than 5%; We Reject H0. Conclusion: Ha is tenable. There is a statistically significant positive correlation between DV (Price) & Predictor (RM, Number of Rooms). 6. ☄ Interpretation of R2= 29.51%. The predictor "RM" explains about 29.51% of "Price"'s variance.
