MBA 604 Introduction Probaility and Statistics Lecture Notes

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T F (vii) The median is a better measure of central tendency than the mean when a distribution is badly skewed. T F (viii) Although we may have a large mass of data, statistical techniques allow us to adequately describe and summarize the data with an average. T F (ix) A sample is a subset of the population. T F (x) A statistic is a number that describes a population characteristic. T F (xi) A parameter is a number that describes a sample characteristic. T F (xii) A population is a subset of the sample. T F (xiii) A population is the complete collection of items under study. 21
Chapter 2 Probability Contents. Sample Space and Events Probability of an Event Equally Likely Outcomes Conditional Probability and Independence Laws of Probability Counting Sample Points Random Sampling 1 Sample Space and Events Definitions Random experiment: involves obtaining observations of some kind Examples Toss of a coin, throw a die, polling, inspecting an assembly line, counting arrivals at emergency room, etc. Population: Set of all possible observations. Conceptually, a population could be generated by repeating an experiment indefinitely. Outcome of an experiment: Elementary event (simple event): one possible outcome of an experiment Event (Compound event): One or more possible outcomes of a random experiment Sample space: the set of all sample points (simple events) for an experiment is called a sample space; or set of all possible outcomes for an experiment Notation. Sample space : S 22
Page 1 and 2: MBA 604 Introduction Probaility and
Page 3 and 4: Contents 1 Data Analysis 5 1 Introd
Page 5 and 6: 10 Simple Linear Regression and Cor
Page 7 and 8: 7. How to interpret polls. How many
Page 9 and 10: Formulas: Note: w = 28.6−5.4 6 w
Page 11 and 12: If n is even, the median is the ave
Page 13 and 14: II. Measures of Variability Given:
Page 15 and 16: x x 2 90 8100 85 7225 65 4225 75 56
Page 17 and 18: (iii) Approximation: s range 4 (iv
Page 19 and 20: z = x − µ σ Example. A set of g
Page 21: (i) Complete all entries in the tab
Page 25 and 26: nA = frequency of the event A nA n
Page 27 and 28: provided P (B) > 0. Similarly, P (B
Page 29 and 30: Thus only 32 percent of those perso
Page 31 and 32: (which is equal to Combinations For
Page 33 and 34: 2. Suppose that S = {1, 2, 3, 4, 5,
Page 35 and 36: (Hint: Start by defining the events
Page 37 and 38: The variable X transforms the probl
Page 39 and 40: 3 Discrete Distributions Binomial.
Page 41 and 42: of D elements of the first kind and
Page 43 and 44: One equation is redundant. Choose t
Page 45 and 46: 4. A random variable X has the foll
Page 47 and 48: 1. List the properties for a binomi
Page 49 and 50: Chapter 4 Continuous Distributions
Page 51 and 52: Z-score: OR (simply) Conversely, X
Page 53 and 54: Exercise. Specialize the above resu
Page 55 and 56: 3. Let Z be a standard normal distr
Page 57 and 58: Chapter 5 Sampling Distributions Co
Page 59 and 60: σ ˆP = S.E.( ˆ pq P )= n (iii)
Page 61 and 62: (ii) Find the probability that the
Page 63 and 64: Single Quantitative Population: µ
Page 65 and 66: where σ is estimated by s. Note: I
Page 67 and 68: The width of the CI decreases. The
Page 69 and 70: Please show all work. No credit for
Page 71 and 72: Chapter 7 Large-Sample Tests of Hyp
Page 73 and 74:
Conclusion: At 100α% significance
Page 75 and 76:
1. Large sample (np ≥ 5,nq≥ 5)
Page 77 and 78:
Test: Sample 2: n2, x2, ˆp2 = x2 n
Page 79 and 80:
orders were recorded, with a sample
Page 81 and 82:
1. Sampled population is normal 2.
Page 83 and 84:
Sample data: Sample 1: n1, x1,s1 Sa
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SUBC> alternative 1. Note: alternat
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RR: Reject H0 if t>2.776 or t X 2
Page 89 and 90:
Q1: Do the data present sufficient
Page 91 and 92:
Training Group 1 2 3 4 65 75 59 94
Page 93 and 94:
GT: Grand Total. Computational Form
Page 95 and 96:
Treatments t1 t2 t3 Bi s1 17 34 23
Page 97 and 98:
Solution to (ii) Test. H0 : µ1 =
Page 99 and 100:
Chapter 10 Simple Linear Regression
Page 101 and 102:
3. The r.v. ɛ has a normal distrib
Page 103 and 104:
Optional material Ad Sales Calculat
Page 105 and 106:
Estimator mean: µ β1 ˆ = β1 Est
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(ii) The population coefficient of
Page 109 and 110:
The regression equation is y=46.5 +
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Source DF SS MS F P Regression 7626
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4. The random components of any two
Page 115 and 116:
MINITAB. Use REGRESS command to reg
Page 117:
Q1. What is the prediction equation
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MBA 604 Introduction Probaility and Statistics Lecture Notes

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