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A z-score is an example of a standardized score. A z-score measures how many standard deviations a data point is from the mean in a distribution.
Course: Statistics and probability > Unit 4. Lesson 2: Z-scores. Normal distribution problem: z-scores (from ck12.org) Comparing with z-scores. Calculating z-scores. Z-scores review.
Course: AP®︎/College Statistics > Unit 10. Lesson 5: Carrying out a test for a population proportion. Calculating a z statistic in a test about a proportion. Calculating the test statistic in a z test for a proportion. Calculating a P-value given a z statistic.
Finding the critical value z* for a desired confidence level. Example constructing and interpreting a confidence interval for p. Calculating a z interval for a proportion. Interpreting a z interval for a proportion. Determining sample size based on confidence and margin of error.
Z-scores-problem. Nutritionists measured the sugar content (in grams) for 32 drinks at Jake's Java coffee shop. The drinks had a mean of 18 g and a standard deviation of 5 g , and the distribution was roughly symmetric. A Grande Mocha Cappuccino at Jake's Java contains 14 g of sugar.
The lesson demonstrates conducting a t-test for a population mean with a teacher experience sample. It explains the t-statistic calculation and the p-value interpretation to test a null hypothesis. This process helps ascertain if the true mean is less than the hypothesized mean. Questions. Tips & Thanks.
Use standardized scores—also called z-scores— to compare data points from different distributions.
Calculate the test statistic in a two-sample z test for the difference of proportions.
Lesson 5: Introduction to the binomial distribution. Binomial variables. Recognizing binomial variables. 10% Rule of assuming "independence" between trials. Identifying binomial variables. Binomial probability example. Generalizing k scores in n attempts. Free throw binomial probability distribution. Graphing basketball binomial distribution.
P-value in a two-sample z test for the difference of proportions. Comparing P value to significance level for test involving difference of proportions. Confidence interval for hypothesis test for difference in proportions. Making conclusions about the difference of proportions.