Techniques of Data Analysis
... Needs interpolation for group/ aggregate data (cumulative frequency curve) May not be characteristic of group when: (1) items are only few; (2) distribution irregular Very limited statistical use ...
... Needs interpolation for group/ aggregate data (cumulative frequency curve) May not be characteristic of group when: (1) items are only few; (2) distribution irregular Very limited statistical use ...
AP Review Inference - Hypotheses Test Key
... level, or increasing the difference that is important to detect between the null and alternative parameter values. Analyze paired data by first taking the difference within each pair to produce a single sample. Then use onesample t procedures. Don’t use t sample t procedures to compare means for pai ...
... level, or increasing the difference that is important to detect between the null and alternative parameter values. Analyze paired data by first taking the difference within each pair to produce a single sample. Then use onesample t procedures. Don’t use t sample t procedures to compare means for pai ...
Ch 16
... we can say that if we did our study over 100 times, we can determine a range within which the sample statistic will fall 95 times out of 100 (95% level of confidence). This gives us confidence that the real population ...
... we can say that if we did our study over 100 times, we can determine a range within which the sample statistic will fall 95 times out of 100 (95% level of confidence). This gives us confidence that the real population ...
Convergence to the Central Limit Theorem
... The distributions for N = 2 and 4 are shown by the short-dashed, and long-dashed lines in the figure. For N = 2, the distribution is a “tent” distribution (consisting of two straight lines; this can be shown analytically). It resembles a Gaussian more then the original rectangular distribution, but ...
... The distributions for N = 2 and 4 are shown by the short-dashed, and long-dashed lines in the figure. For N = 2, the distribution is a “tent” distribution (consisting of two straight lines; this can be shown analytically). It resembles a Gaussian more then the original rectangular distribution, but ...
ch07
... Name: 1. A population contains eight members. The total number of samples of size 3 that can be drawn (without replacement) from this population is: a. 40,320 b. 56 c. 6 d. 210 2. The sampling error is defined as: a. an error that occurs during collection, recording, and tabulation of data b. the di ...
... Name: 1. A population contains eight members. The total number of samples of size 3 that can be drawn (without replacement) from this population is: a. 40,320 b. 56 c. 6 d. 210 2. The sampling error is defined as: a. an error that occurs during collection, recording, and tabulation of data b. the di ...
Exploring.Data.Intro
... The first quartile ( Q1 ) is the value for which 25% of the observations are less than. It is the Median of the first half of the set of observations. The third quartile ( Q3 ) is the value for which 75% of the observations are less than. It is the Median of the second half of the set of observation ...
... The first quartile ( Q1 ) is the value for which 25% of the observations are less than. It is the Median of the first half of the set of observations. The third quartile ( Q3 ) is the value for which 75% of the observations are less than. It is the Median of the second half of the set of observation ...
Measures of Center
... Q: In figuring Phyllis’ grade, her instructor needs to know total HW points she scored but lost this info. Which would be more useful to him a) her mean score ...
... Q: In figuring Phyllis’ grade, her instructor needs to know total HW points she scored but lost this info. Which would be more useful to him a) her mean score ...
Activity 7.4.1A – Exploring Distributions of Sample Means
... population parameters. Sample statistics are numerical descriptions of sample characteristics (e.g. sample mean, sample proportion). Population parameters are numerical descriptions of population characteristics (e.g. population mean, population proportion). When population parameters are unknown we ...
... population parameters. Sample statistics are numerical descriptions of sample characteristics (e.g. sample mean, sample proportion). Population parameters are numerical descriptions of population characteristics (e.g. population mean, population proportion). When population parameters are unknown we ...
Solution to Homework #2
... temperature (degrees Fahrenheit) of 130 adults, 65 males and 65 females. The data are in a file on the course web page. Follow the instructions in the JMP Guide to download the data from the web and open the data in JMP. a) Obtain a histogram, box plot, stem-and-leaf display and descriptive statisti ...
... temperature (degrees Fahrenheit) of 130 adults, 65 males and 65 females. The data are in a file on the course web page. Follow the instructions in the JMP Guide to download the data from the web and open the data in JMP. a) Obtain a histogram, box plot, stem-and-leaf display and descriptive statisti ...
Ch7 - YSU
... population of size N is a sample selected such that each possible sample of size n has the same probability of being selected. Replacing each sampled element before selecting subsequent elements is called sampling with replacement. Sampling without replacement is the procedure used most often. I ...
... population of size N is a sample selected such that each possible sample of size n has the same probability of being selected. Replacing each sampled element before selecting subsequent elements is called sampling with replacement. Sampling without replacement is the procedure used most often. I ...
In an opinion poll, 25% of 200 people sampled said that they
... chance of being selected. (g) Each member of the population is given an opportunity to respond to the survey. (h) All samples of size n have the same chance of being selected. (i) The probability of selecting any sample is known to be 7 rand . (j) The sample is guaranteed to represent the entire p ...
... chance of being selected. (g) Each member of the population is given an opportunity to respond to the survey. (h) All samples of size n have the same chance of being selected. (i) The probability of selecting any sample is known to be 7 rand . (j) The sample is guaranteed to represent the entire p ...
simple_lin_regress_inference
... independent variable emerged by chance and does not describe a real relationship. A p-value of .05 means that there is a 5% chance that the relationship emerged randomly and a 95% chance that the relationship is real. It is generally accepted practice to consider variables with a p-value of less tha ...
... independent variable emerged by chance and does not describe a real relationship. A p-value of .05 means that there is a 5% chance that the relationship emerged randomly and a 95% chance that the relationship is real. It is generally accepted practice to consider variables with a p-value of less tha ...
Measure of central tendency
... A type I error is made when the researcher rejected the null hypothesis when it should not have been rejected. A type II error is made when the null hypothesis is accepted when it should have been rejected. ...
... A type I error is made when the researcher rejected the null hypothesis when it should not have been rejected. A type II error is made when the null hypothesis is accepted when it should have been rejected. ...
Book Chapter 3
... CANNOT be summarized using the mean and standard deviation or the median and interquartile range. ...
... CANNOT be summarized using the mean and standard deviation or the median and interquartile range. ...
Bootstrapping (statistics)
In statistics, bootstrapping can refer to any test or metric that relies on random sampling with replacement. Bootstrapping allows assigning measures of accuracy (defined in terms of bias, variance, confidence intervals, prediction error or some other such measure) to sample estimates. This technique allows estimation of the sampling distribution of almost any statistic using random sampling methods. Generally, it falls in the broader class of resampling methods.Bootstrapping is the practice of estimating properties of an estimator (such as its variance) by measuring those properties when sampling from an approximating distribution. One standard choice for an approximating distribution is the empirical distribution function of the observed data. In the case where a set of observations can be assumed to be from an independent and identically distributed population, this can be implemented by constructing a number of resamples with replacement, of the observed dataset (and of equal size to the observed dataset).It may also be used for constructing hypothesis tests. It is often used as an alternative to statistical inference based on the assumption of a parametric model when that assumption is in doubt, or where parametric inference is impossible or requires complicated formulas for the calculation of standard errors.