Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
History of statistics wikipedia , lookup
Foundations of statistics wikipedia , lookup
Psychometrics wikipedia , lookup
Confidence interval wikipedia , lookup
Bootstrapping (statistics) wikipedia , lookup
Taylor's law wikipedia , lookup
German tank problem wikipedia , lookup
Misuse of statistics wikipedia , lookup
TI Menu TI 84 Calculator Commands AP Statistics Command & Parameters Stat/Calc 2nd Distr. 2nd Distr Stat/Calc #4 1-Var Stats L1 Normalcdf(left,right,Mean,SD) InvNorm(Area below z-score) LinReg (ax+b) Xlist, Ylist Stat Plot/Plot 1 Scatterplot(xlist,Resid) Math/Prb Stat/Calc 2nd Distr RandInt(left,right,# of rand. Digits) 1-Var Stats L1,L2 L1 is values of Variables (outcomes) L2 is Probability of each outcome Geompdf(p,x) p is probability x is # of the first success Geomcdf(p,x) p is probability x is # of the first success Binompdf(n, p, x) n is # of trials p is probability of success x is desired # of successes Binomcdf(n, p, x) n is # of trials p is probability of success x is desired # of successes 1-Prop Z-Interval Enter successes, sample size, & confidence level 1-Prop Z-Test P0 is population proportion X is observed # of successes in sample n is sample size 2-Prop Z-Test X1 is observed successes in sample 1 (Whole #) n1 is Sample 1 size x2 is Observed successes in Sample 2 (Whole #) n2 is sample 2 size 2-Prop Z-Interval X1 is observed successes in sample 1 (Whole #) n1 is Sample 1 size x2 is Observed successes in Sample 2 (Whole #) n2 is sample 2 size Confidence Level tcdf(left,right,df) 2nd Distr Invt(Area below t-score,df) Stat/Test T-interval Data L1 Stats x is sample mean Sx is Sample Standard Deviation N is sample size 2nd Distr 2nd Distr 2nd Distr 2nd Distr Stat/Test Stat/Test Stat/Test Stat/Test Use Gives Summary stats for Mean/SD, Median/IQR, Max, Min, etc… Area under curve between 2 bounds Gives the z-score based on the area to the Gives Regression equation, r & r2 between 2 lists. (Diagnostic must be turned on in Catalog) Scatterplot of the Residuals to check appropriateness of Linear Regression Model Generates Random Numbers Calculates Mean and Standard Deviation of a Random Variable Probability that a specific outcome is the first success (Probability that x is the first success) Probability that the first success happens on or before the xth trial Probability of getting exactly x successes in n trials Probability of getting x or fewer successes in n trials Will calculate the confidence interval Will run the 1 proportion z-test and give you z-score, P-value and other statistics Test for 2 proportions will give you summary statistics between the 2 samples Test for 2 proportions will give you the confidence interval for the difference between the 2 samples Gives the area under a t-model curve between the specific bounds for the degrees of freedom Gives the critical t-score when entering the area below the tscore and the degrees of freedom Finds the T-interval for data(list) or statistics when given 1 sample Stat/Test Stat/Test Stat/Test Stat/Test Stat/Test Stat/Test Stat/Test Stat/Test T-Test U0 is population mean 2-Samp T Int Data: Put the samples in L1 & L2 Stats: xx & x2 is sample means Sx1 & Sx2 is Sample Standard Deviations n1 & n2 is sample size Hypothesis Test for 1 sample means. Gives p-value, t-score, etc… 2-Samp T Test Data: Put the samples in L1 & L2 Stats: xx & x2 is sample means Sx1 & Sx2 is Sample Standard Deviations n1 & n2 is sample size T-interval Data L1 Stats x is sample mean Sx is Sample Standard Deviation N is sample size T-Test U0 is population mean Test the difference between two independent samples for the mean and provides test statistics. Gives p-value, t-score, etc… DO NOT POOL THE SAMPLES X2 GOF Test L1 is observed counts (Whole numbers) L2 is expected counts (May be decimals) X2 Test [A]: Matrix A is Observed Counts Run the test and look at [B] to see expected counts LinReg T-Test or LinReg T-interval L1 is X list L2 is Y list Run the 2 tail test with ≠ 0 or the interval with the confidence level Find the confidence interval for the 2 difference between two independent samples for the mean. DO NOT POOL THE SAMPLES Use a one sample interval for matched pairs (2 samples that are not independent). You find the difference between the 2 samples and this difference is your one list to test. Use a one sample interval for matched pairs (2 samples that are not independent). You find the difference between the 2 samples and this difference is your one list to test. Gives p-value, t-score, etc… Goodnes of fit test for categorical variables. To check if a distribution is the same as what you would expect (Is a sample the same as the population?) Categorical Data for homogeneitity (are 2 samples the same?) or independence (are 2 variables associated?) T-Test to find out if 2 lists are associated through linear regression Find the Confidence Interval for a level of confidence and degrees of freedom