I would like an explanation and example of two statistical methods:
1. Parametric method:
- Hypothesis ( null and alternative)
- Simple example and explanation in layman's terms
- Confidence level 90%
- Need the critical value explains how attained
- Need Z-score and explained if appropriate ( think this comes from the table, i.e., 90% should be 1.28???
- Compare each data point to the claimed value and sum these. Then compare the critical value and test statistic
- Do we accept or reject Ho or H1 at the stated level of confidence and why? Made in a statement.
2. Nonparametric method:
- Run test
- Simple example and explanation using zero (0) and (1) Ten runs...
(11111 000 11 0000 11111 00 111 00000 1111 000)
- Be sure to include whether it exceeds critical value and why
- Test for randomness (explain)
If you could provide me with some references for further understand that would be good.
In summation I want to be able to discuss the goals of two statistical methods.© BrainMass Inc. brainmass.com October 16, 2018, 8:00 pm ad1c9bdddf
The solution provides step by step method for various parametric and non parametric methods
Explains the analysis of variance, multivariate statistics, and non-parametric methods
Company W is testing a sales software. Their salesforce of 500 people is divided into four regions: Northeast, Southeast, Central and West. Each sales person is expected to sell the same amount of products. During the last 3 months, only half of the sales representatives in each region were given the software program to help them manage their contacts.
The VP of Sales at WidgeCorp, who is comfortable with statistics, wants to know the possible null and alternative hypotheses for a non-parametric test on this data using the chi-square distribution. A non-parametric test is used on data that is qualitative or categorical, such as gender, age group, region, and color. It is used when it doesn't make sense to look at the mean of such variables.