sPSS
1. The definition of hypothesis testing
2. significant test principle
3. The basic idea of hypothesis testing
4. hypothesis testing two types of errors
5. hypothesis testing reasons make two types of errors
Step 6. hypothesis testing
7. Mean Process Concepts
8. Process effect Mean
9. have a choice of sub-set of statistics for each category of each packet from the mean of the process variable
1 is used to determine the sample with the sample, the sample with the overall difference is caused by the sampling error or statistical inference method is essentially caused by differences
2. The first is to make certain assumptions about the general characteristics of the study sample and then through statistical inference, this hypothesis should be rejected or accepted to make inferences
3. reductio ad absurdum and small probability principle.
Reductio ad absurdum is to test the hypothesis put forward, then the appropriate statistical methods, the use of small probability principle, to determine whether the hypothesis holds. Small probability principle basically refers to the small probability events will not occur in a trial.
4. Type I error: null hypothesis is correct, but wrong to refuse it, that "true refused" error, the probability of its occurrence as a Type I error probability
Type II error: null hypothesis is not correct, but the error did not refuse it, namely, "The Pseudo" error, the probability of its occurrence as a Type II error probability
The small probability of time is not impossible, but probability of its occurrence is very small, we can not completely exclude the possibility of its occurrence.
Determine the appropriate null hypothesis and alternative hypothesis
6. Select the test statistic, and the calculated value with the selected samples to obtain observations of the test statistic probability that the observed value of the test statistic calculation occurs, i.e., the value of p
Given the significance level a, and make decisions. If p <a, the null hypothesis is rejected, on the contrary, there is no reason to reject the null hypothesis
7. Describe and analyze a useful method for scale variables, you can get a lot of central tendency and statistical indicators need to analyze the trend of discrete variables, and he can compare the different groups and constituencies or post
8. The process may calculate one or more independent variables category due subgroup averages and related univariate statistical variables, you can also get one-way ANOVA and correlation test limit line from the process.
9. total, the number of cases, mean, median. Groups in the median, standard error of the mean, minimum, maximum, range, a first category variable value of the variable packet, the last packet of a variable category variable value, standard deviation, variance, kurtosis, a kurtosis standard error, skewness, bias error criterion. The percentage of the sum of the percentage of the total, percentage, and the percentage of the number, geometric mean, harmonic mean.