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The Null Hypothesis - H0: The random errors will be normally distributed
The Alternative Hypothesis - H1: The random errors are not normally distributed
Reject H0: when P-value ≤ α = 0.05
As the P value is 0.043 it is less than the 0.05 significance level therefore reject H0 and accept H1 as there is sufficient evidence to show that random errors are not normally distributed. The assumption of normality is possibly satisfied as the normal probability plot is close to the straight line.
Mean Absolute Deviation To avoid the problem of positive and negative deviations canceling out each other, we can use the Mean Absolute Deviation which is given by
Flow Chart for Confidence Interval We can now prepare a flow chart for estimating a confidence interval for μ, the population parameter. Figure
entropy test to measure interaction between enviornmental factors and genes
Your company has developed a new product .Your company is a reputed company with 50% market share of same range of products. Your competitors also come with their new products equa
Advantages It is especially useful in case of open-end classes since only the position and not the values of items must be known. The median is also recommended if th
Hi There, I have a question regarding R, and I am wondering if anyone can help me. Here is a code that I would like to understand: squareFunc g f(x)^2 } return(g) } sin
Linear Programming
The Null Hypothesis - H0: β0 = 0, H0: β 1 = 0, H0: β 2 = 0, Β i = 0 The Alternative Hypothesis - H1: β0 ≠ 0, H0: β 1 ≠ 0, H0: β 2 ≠ 0, Β i ≠ 0 i =0, 1, 2, 3
What would be the cutoff score to indicate a score that is in the top 15% of the scores on a test with a mean of 100 and a standard deviation of 15? This question has multiple p
Examples of grouped, simple and frequency distribution data
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