Chapter 3 - Math TAMU
... Discrete finite variables - graph the probability as a histogram. Each rectangle has a base of width 1 (centered on X) and the height was P(X). So the area, length x height was the probability that X occurred. AREA above our X value will be the probability that get that X value. If we want to find t ...
... Discrete finite variables - graph the probability as a histogram. Each rectangle has a base of width 1 (centered on X) and the height was P(X). So the area, length x height was the probability that X occurred. AREA above our X value will be the probability that get that X value. If we want to find t ...
Statistical Tests
... Parametric tests – Used for analyzing interval & ratio variables – Makes assumptions about data ...
... Parametric tests – Used for analyzing interval & ratio variables – Makes assumptions about data ...
Statistics
... = total time customers wait in queue total number of customers Average wait time of those who wait = total time of customers who wait in queue number of customers who wait ...
... = total time customers wait in queue total number of customers Average wait time of those who wait = total time of customers who wait in queue number of customers who wait ...
TUTORIAL 1 1) A random car is chosen among all
... 1) A random car is chosen among all those passing through The Store on a certain day. The probability that the car is red is 0.4, the probability that the driver is a student is 0.3 and the probability that the car is red and the driver is a student is 0.05. A car is selected at random, calculate th ...
... 1) A random car is chosen among all those passing through The Store on a certain day. The probability that the car is red is 0.4, the probability that the driver is a student is 0.3 and the probability that the car is red and the driver is a student is 0.05. A car is selected at random, calculate th ...
Probability PowerPoint
... • The values in a row of Pascal's triangle are the coefficients in a binomial expansion of the same degree as the row. • A binomial expansion of degree n is (a + b)n. • The variables are anb0 + an-1b1 + … + a1bn-1 + anb0 + a0bn ...
... • The values in a row of Pascal's triangle are the coefficients in a binomial expansion of the same degree as the row. • A binomial expansion of degree n is (a + b)n. • The variables are anb0 + an-1b1 + … + a1bn-1 + anb0 + a0bn ...
review - Penn State Department of Statistics
... making a Type I error to be small (0.05 or 0.01). • Compare the value of the test statistic to the known distribution of the test statistic. • If the test statistic is more extreme than expected, allowing for an α chance of error, reject the null hypothesis. Otherwise, don’t reject the null. ...
... making a Type I error to be small (0.05 or 0.01). • Compare the value of the test statistic to the known distribution of the test statistic. • If the test statistic is more extreme than expected, allowing for an α chance of error, reject the null hypothesis. Otherwise, don’t reject the null. ...
Review of key statistical concepts - Penn State Department of Statistics
... making a Type I error to be small (0.05 or 0.01). • Compare the value of the test statistic to the known distribution of the test statistic. • If the test statistic is more extreme than expected, allowing for an α chance of error, reject the null hypothesis. Otherwise, don’t reject the null. ...
... making a Type I error to be small (0.05 or 0.01). • Compare the value of the test statistic to the known distribution of the test statistic. • If the test statistic is more extreme than expected, allowing for an α chance of error, reject the null hypothesis. Otherwise, don’t reject the null. ...
Bayesian Statistics
... The principle of Bayes model is to compute posteriors based on specified priors and the likelihood function of data. It requires researchers to appropriately specify priors given inappropriate priors may lead to biased estimates or make computation of posteriors difficult. In this section, we will b ...
... The principle of Bayes model is to compute posteriors based on specified priors and the likelihood function of data. It requires researchers to appropriately specify priors given inappropriate priors may lead to biased estimates or make computation of posteriors difficult. In this section, we will b ...
26 Hypothesis Testing
... We can’t just say that the drug is clearly better because 75% of patients improved compared with 60% before. We need to find out what the probability is that 15 patients improve even if the new drug is no more effective than the old one. The situation can be modelled with the Binomial distribution. ...
... We can’t just say that the drug is clearly better because 75% of patients improved compared with 60% before. We need to find out what the probability is that 15 patients improve even if the new drug is no more effective than the old one. The situation can be modelled with the Binomial distribution. ...
9 Tests and Confidence Intervals
... We need a critical region (or rejection region). This is the set of values of Z which will lead us to reject the null hypothesis in favour of the alternative. In this example the critical region will have the form Z < k (since small values of Z would be expected under the alternative hypothesis). Th ...
... We need a critical region (or rejection region). This is the set of values of Z which will lead us to reject the null hypothesis in favour of the alternative. In this example the critical region will have the form Z < k (since small values of Z would be expected under the alternative hypothesis). Th ...