Lecture #13: Confidence Intervals for the Mean
... what’s called a confidence interval by employing a number called E, and saying that we are confident to a certain degree (which we will go into in great detail) that the population mean falls between x E and x E . We could also use the double inequality notation of algebra: x E x E . T ...
... what’s called a confidence interval by employing a number called E, and saying that we are confident to a certain degree (which we will go into in great detail) that the population mean falls between x E and x E . We could also use the double inequality notation of algebra: x E x E . T ...
Forelesning om beskr. stat. etc
... Statistics estimates unknown parameters (like the mean of a population). Parameters represent things that are unknown. They are some properties of the population from which the data arise. Questions of interest are expressed as questions on such parameters: confidence intervalls, hypothesis testing ...
... Statistics estimates unknown parameters (like the mean of a population). Parameters represent things that are unknown. They are some properties of the population from which the data arise. Questions of interest are expressed as questions on such parameters: confidence intervalls, hypothesis testing ...
Document
... Excel Note: It seems most of you are using excel and are not experiencing too many problems. The problems will become a little more challenging and excel will really help. Do practice though –it will help. You can get information about any statistical procedure by typing the name of the procedure i ...
... Excel Note: It seems most of you are using excel and are not experiencing too many problems. The problems will become a little more challenging and excel will really help. Do practice though –it will help. You can get information about any statistical procedure by typing the name of the procedure i ...
12.0 Lesson Plan - Duke Statistical
... Because we are asking about the mean, because the sample size is small, and because we must estimate the population sd from the sample sd, then we are in Case 2. Our critical value comes from a Student’s t-table with 15 - 1 = 14 df, and area under the curve 0.10 (since the confidence is 90%, the err ...
... Because we are asking about the mean, because the sample size is small, and because we must estimate the population sd from the sample sd, then we are in Case 2. Our critical value comes from a Student’s t-table with 15 - 1 = 14 df, and area under the curve 0.10 (since the confidence is 90%, the err ...
Lecture 2 Stemplots
... • In any graph of data, look for the overall pattern and for striking deviations from that pattern. • Overall pattern of a distribution can be described by its shape, centre, and spread. • An important kind of deviation is an outlier, an individual value that falls outside the overall pattern. • Som ...
... • In any graph of data, look for the overall pattern and for striking deviations from that pattern. • Overall pattern of a distribution can be described by its shape, centre, and spread. • An important kind of deviation is an outlier, an individual value that falls outside the overall pattern. • Som ...
sampling distribution
... Sampling Distributions • We expect variability when we sample. Because of this we create a set of values that fall around the center of the distribution, p. This variability creates a curve that under the right conditions will be approximately normal. ...
... Sampling Distributions • We expect variability when we sample. Because of this we create a set of values that fall around the center of the distribution, p. This variability creates a curve that under the right conditions will be approximately normal. ...
Three Broad Purposes of Quantitative Research
... • Range: the difference between the highest and lowest scores in a distribution of scores. • Variance: a measure of dispersion indicating the degree to which scores cluster around the mean score. • Standard deviation: index of the amount of variation in a distribution of scores. ...
... • Range: the difference between the highest and lowest scores in a distribution of scores. • Variance: a measure of dispersion indicating the degree to which scores cluster around the mean score. • Standard deviation: index of the amount of variation in a distribution of scores. ...
STATISTICS - SUMMARY - Michigan State University
... variables AND a test of null hypothesis that the correlation in population is zero. Be sure you understand distinction here between the measure of association between the two variables in the sample (correlation coefficient) and the test of hypothesis that correlation is zero (making inference to th ...
... variables AND a test of null hypothesis that the correlation in population is zero. Be sure you understand distinction here between the measure of association between the two variables in the sample (correlation coefficient) and the test of hypothesis that correlation is zero (making inference to th ...
Estimation - users.miamioh.edu
... 1. Sample statistic & standard error calculated from sample data 2. t or z will be selected, based on some criterion 3. For a point estimate, your best estimate of the population parameter is the sample statistic (e.g, X is the best point estimate of ) 4. For an interval estimate, your best estimat ...
... 1. Sample statistic & standard error calculated from sample data 2. t or z will be selected, based on some criterion 3. For a point estimate, your best estimate of the population parameter is the sample statistic (e.g, X is the best point estimate of ) 4. For an interval estimate, your best estimat ...
Examples - Solon City Schools
... If a group has a low outlier, the curve has a (contains more high If most students scored well on a test, what would the distribution look scores) ...
... If a group has a low outlier, the curve has a (contains more high If most students scored well on a test, what would the distribution look scores) ...
Ch7-Sec7.3
... with n – 1 degrees of freedom. If the hypothesis test is a. left-tailed, use “One Tail, ” column with a negative sign, b. right-tailed, use “One Tail, ” column with a positive sign, c. two-tailed, use “Two Tails, ” column with a negative and a ...
... with n – 1 degrees of freedom. If the hypothesis test is a. left-tailed, use “One Tail, ” column with a negative sign, b. right-tailed, use “One Tail, ” column with a positive sign, c. two-tailed, use “Two Tails, ” column with a negative and a ...
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.