000			03489nam a22002657a 4500
005			20250514142320.0
020			_a 0367580314 _cTZS 177,567/=
040			_aMUL _beng. _eAACR2
082			_a519.5 CHA
100			_aCHALMER, Bruce J.
245			_aUnderstanding statistics / _bBruce J. Chalmer
260			_aLondon: _bCRC Press, _c2019.
300			_ax, 326p. : _bill. ; _c27 cm.
500			_aIncludes Statpal (statistical package for microcomputers) PC-DOS version 5.0 for IBM PC and compatibles (M-iii-M89).
504			_aIncludes references and index (M-91-M-96)
505			_aPreface -- 1 Variables, Population, and Samples -- 1.1 Statistics is concerned with describing and explaining how things vary -- 1.2 Inferences about populations based on sample information are subject to error -- 1.3 Other forms of random samples include stratified and cluster sampling -- 2 Basic Ideals of Statistical Inference -- 2.1 There are two basic tasks of statistical inference -- 2.2 To evaluate degree of certainty, first consider all possible samples -- 2.3 To find degree of certainty, find the sampling distribution of the statistic -- 2.4 A handy way of representing a distribution is to draw a histogram -- 2.5 The larger the sample, the less spread out is the sampling distribution -- 2.6 So what? -- 3 Describing Data for a Single Variable -- 3.1 There are many ways of summarizing a set of data -- 3.2 Often we are interested in the entire population distribution -- 3.3 Some distributions can be approximated by mathematical functions -- 3.4 A histogram and frequency distribution are very useful descriptive tools -- 3.5 In a histogram, area represents relative frequency -- 3.6 The mean, median, and mode are useful for describing central tendency -- 3.7 The range and interquartile range are measures of variability -- 3.8 The standard deviation is the most commonly used measure of variability -- 3.9 The standard deviation is computed differently for samples versus populations -- 4 Some Distributions Used in Statistical Inference -- 4.1 Knowing the sampling distribution of a statistic allows us to draw inferences from sample data -- 4.2 The standard normal distribution is used to find areas under any normal curve -- 4.3 The binomial distribution is used for variables that count the number of yeses -- 4.4 To calculate binomial probabilities, we need to find the probability of each possible outcome -- 4.5 We next find the number of relevant outcomes and multiply it by the probability of each relevant outcome -- 4.6 Binomial probabilities can be computed from a general formula and are also available in tables -- 4.7 A binomial distribution with large n and moderate p is approximately normal -- 5 Interval Estimation -- 5.1 The standard error of a statistic is the standard deviation of its sampling distribution -- 5.2 The CLT can be applied to draw inferences about the population mean -- 5.3 If we know
520			_aIntroducing undergraduates to the vital concepts of statistics, this superb textbook allows instructors to include as much mathematical detail as may be suitable for their students. Featuring Statpal statistical software for the IBM PC®, the book contains study questions that help solidify students’ understanding.
546			_aeng.
650			_aStatistical inference
650			_aRelationships between variables
650			_aRegression methods
650			_aHypothesis testing
650			_aInterval estimation
942			_cBK
999			_c10873 _d10873