STAT 210 1st EditionGradeBuddyLecture 22Last LectureI. t-Distributions and Sampling DistributionsThis LectureII. Introduction to confidence intervalsIII. InferenceIV. Test of significanceCurrent LectureV. Statistical inference involves using statistics computed from data collected in a sample to make statements (inferences) about unknown population parametersVI. Two types of statistical inference are estimation of parameters using confidence intervals and statistical tests about parametersVII. Confidence intervals are statistical procedures that allow for the estimation ofunknown population parametersVIII. To estimate an unknown population parameter we begin by selecting a sample from the population. Once the sample is selected, we then collect thenecessary information from those in the sample.IX. The data collected from the sample is used to compute a statistic, and this statistic becomes the starting point for the confidence interval and hence the statistical inference.X. The general form of a confidence interval for a parameter isXI. point estimate + margin of errorXII. The margin of error will be calculated from the sampling distribution of the point estimate being used XIII. When directed to compute a confidence interval for an unknown parameter, one usually specifies the amount of confidence that is desired.XIV. The most common values are 90% confidence, 95% confidence, 98% confidence and 99% confidence, but any value above 0% and less than 100% could be used. XV. The amount of confidence is called the confidence level.XVI. The confidence interval is interpreted by stating that we have 100 * C% confidence that the unknown population parameter falls between the lower limit, XVII. L = point estimate – margin of error, and the upper limit, U = point estimate + margin of error.XVIII. The width of a confidence interval is twice the margin of error: width = 2 * margin of error.STAT 210 1st EditionXIX. With statistical tests we conjecture that the unknown population parameter equals some value (referred to as a statistical hypothesis) and then we use the data in the sample to test whether this value is reasonable or not. XX. The null hypothesis, denoted by H0, is a conjecture about a population parameter that is presumed to be true. It is usually a statement of no effect or no change.XXI. The alternative (or research) hypothesis, denoted by Ha or H1, is a conjecture about a population parameter that the researcher suspects or hopes is true.XXII. The null hypothesis H0 will always contain an equality statement (an equal to sign: