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BASIC STATISTICS For the HEALTH SCIENCES Fifth Edition






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BASIC STATISTICS For the HEALTH SCIENCES Fifth Edition. By Kuzma. “It’s all Greek to me!!!!”.  = Summation. x x x x x x. 5 4 6 5 2 8. . . . . . . . .  x i. = 30. CHAPTER 4. Summarizing Data . OUTLINE. 4.1 MEASURES OF CENTRAL TENDENCY
BASIC STATISTICS For the HEALTH SCIENCES Fifth Edition

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Slide 1

BASIC STATISTICSFor the HEALTH SCIENCESFifth Edition

By

Kuzma

Slide 2

“It’s all Greek to me!!!!”

 = Summation

x

x

x

x

x

x

5

4

6

5

2

8

 xi

= 30

Slide 3

CHAPTER 4

Summarizing Data

Slide 4

OUTLINE

4.1MEASURES OF CENTRAL TENDENCY

Explains why the selection of an appropriate sample has an imprint bearing the reliability of inferences about a population

4.2MEASURES AND VARIATION

  Describes several measure of variation or variability including the standard deviation

4.3COEFFICIENT OF VARIATION

Defines the coefficient of variation, useful in comparing levels of variation

4.4MEASURING AND INTERPRETING SKEWNESS

Explains how to measure skewness and how to determine if a distribution is symmetrical or skewed

MEANS AND STANDARD DEVIATIOS OF POPULATIONS

Contrasts the equations for the parameters of a population to the statistics of a sample

Slide 5

LEARNING OBJECTIVES

  • 1.Compute and distinguish between the uses of measures of central tendency: mean, median, and mode

  • 2.Compute and lists some uses for measures of variation: range, variance, and standard deviation

  • 3.Compare sets of data by computing their coefficients of variation

  • 4.Be able to compute the mean and standard deviation for grouped and ungrouped data

  • 5.Determine if a data set is symmetrical or skewed

  • 6.Understand the distinction between the population mean and the sample mean

Slide 6

MEASURES OF CENTRAL TENDENCY

  • A.The Mean

    • 1.the arithmetic or simple mean is computed by sunning all the observations in the sample and dividing the sum by the number of observations

    • 2.there are also harmonic and geometric means

    • 3.The arithmetic mean may also be considered the balance point, or, fulcrum

  • B.The Median

    • 1.the observation that divides the distribution into equal parts

    • 2.considered the most typical observation in the a distribution

    • 3.that value above which there are the same number of observations below

    • 4.Symbolically the mean is represented by

Slide 7

MEASURES OF CENTRAL TENDENCY

  • C.The Mode

    • 1.Observation that occurs most frequently

    • 2.If all values are different, there is no mode

  • D.Which Average Should You Use

    • 1.Arithmetic mean is the most commonly used

    • 2.Median gives the typical observation for a distribution – good for income

Slide 8

MEASURES AND VARIATION

  • A.Range

    • 1.the difference in value between the highest (maximum) and lowest (minimum) observation

      • Range =

  • 2.can be computed easily but is not very useful because it considers only the extremes

Slide 9

MEASURES AND VARIATION

  • B.Mean Deviation

  • 1.the average deviation of all observations from the mean

  • 2.the sum of all of the absolute values divided by the number of observations

  • Mean Deviation =

  • or

Slide 10

MEASURES AND VARIATION

  • C.Standard Deviation

    • 1.by far the most widely used measure of variation

    • 2.the square root of the variance of the observations

    • 3.computed by:

      • -squaring each deviation from the mean

      • -adding them up

      • - dividing their sum by less than the sample size

Slide 11

MEASURES AND VARIATION

or

s =

Slide 12

Standard Deviation = 4.43

Slide 13

Variance ------Standard Deviation

Variance = Standard Deviation2

Variance = Standard Deviation

Slide 14

Raw data and results of Cholesterol levels in 26 subjects

Number of observations or N 26

Initial HDL values 31, 41, 44, 46, 47, 47, 48, 48, 49, 52, 53, 54, 57, 58, 58, 60, 60, 62, 63, 64, 67, 69, 70, 77, 81, 90 mg/dl

Highest values 90 mg/dl

Lowest value 31 mg/dl

Mode 47, 48, 58, 60 mg/dl

Median (57 + 58)/2 = 57.5 mg/dl

Sum of the values  (xi) 1496 mg/dl

Means, x 1496/26 = 57.5 mg/dl

Interquartile range 64 – 48 = 16 mg/dl

Slide 15

 (|xi - x|)

N

2

 (xi - x )

2

s

=

Degrees of Freedom

N -1

2

 (xi - x )

N -1

Measures of dispersion based on the Mean.

  • Mean deviation =

  • Variance =

  • Standard deviation = s =

Slide 16

Raw data and results of Cholesterol levels in 26 subjects

Number of observations or N = 26

Initial HDL values 31, 41, 44, 46, 47, 47, 48, 48, 49, 52, 53, 54, 57, 58, 58, 60, 60, 62, 63, 64, 67, 69, 70, 77, 81, 90 mg/dl

Highest values 90 mg/dl

Lowest value 31 mg/dl

Mode 47, 48, 58, 60 mg/dl

Median (57 + 58)/2 = 57.5 mg/dl

Sum of the values  (xi) 1496 mg/dl

Means, x 1496/26 = 57.5 mg/dl

Interquartile range 64 – 48 = 16 mg/dl

Sum of squares (TSS) 4,298.46 mg/dl squared

Variance, “s” squared 171.94 mg/dl

Standard Deviation, s 171.94 mg/dl = 13.1 mg/dl

Slide 17

MEASURES AND VARIATION

  • D.Computing Central Tendency Using SPSS

    • 1.To calculate mean and standard deviation:

      • a.go to the menu and choose Analyze

      • b.Select Descriptives

      • - select variables you want to analyze

Slide 18

COEFFICIENT OF VARIATION

  • A.Defined as the ratio of the standard deviation to the absolute value of the mean, expressed as a percentage

    • CV =

  • B.Depicts the size of the standard deviation in comparison to its mean

  • C.Possible to use it to compare the relative variation of even unrelated quantities

  • Slide 19

    MEASURING AND INTERPRETING SKEWNESS

    • A.Skewed – a distribution that has a marked or pronounced asymmetry

    • B.Use SPSS to measure skewness

      • 1.Open dialog box titled Options

      • 2.Select Skewness

      • 3.Two columns under skewness

        • a.Statistic

        • b.Std Error

      • 4. The Statistics column gives the actual measure of skewness, or the standard error of skewness

    Slide 20

    MEANS AND STANDARD DEVIATIONS OF POPULATIONS

    • A.Population Mean

      • 1.defined as the sum of the values divided by the number of observations for the entire population (N)

      • 2.it is the sum of the squared deviations from the population mean, divided by N

      • 3.The sample mean (x) is an estimate of the population mean and is the sum of values in the sample divided by n, the number of observations in the sample alone

      • 4.The population variance is the is the sum of the squared deviations from the population mean divided by N, whereas the sample variance is the sum of the squared deviations from the sample mean

    Slide 21

    CONCLUSION

    In describing data by use of a summary measure, it is important to select the measure of central tendency that most accurately represents the data. A key factor is to determine of the data are symmetrical or skewed. Data are most commonly represented by two summary measures – one to indicate central tendency and one to indicate variation. The most commonly used pair is the arithmetic mean and the standard deviation.

    Slide 22

    CHAPTER 5

    Probability

    Slide 23

    OUTLINE

    5.1 WHAT IS PROBABILITY

    Discusses the concept of probability as a measure of the likelihood of occurrence of a particular event

    5.2 COMPLEMENTARY EVENTS

    Demonstrates how to calculate probability when the events are complementary

    5.3 PROBABILITY RULES

    Solves problems involving the probability of compound events by use of the addition rule or the multiplication rule, or conditional probability

    5.4 COUNTING RULES

    Explains how to compute the number of possible ways an event can occur by use of permutations and combinations

    5.5 PROBABILITY DISTRIBUTIONS

    Illustrates the concept of a probability distribution, which lists the probabilities associated with the various outcomes of a variable

    5.6 LOTTERY PROBABILITY AND SAMPLING

    Uses lottery probabilities to illustrate sampling with and without replacement

    5.7 BINOMIAL DISTRIBUTION

    Describes a common distribution having only two possible outcomes on each trial

    Slide 24

    LEARNING OBJECTIVES

    • 1.Define probability and compute it in a given situation

    • 2.State the basic properties of probability

    • 3.Select and apply the appropriate probability rule for a given situation

    • 4.Distinguish between mutually exclusive events and independent events

    • 5.Distinguish between permutations and combinations, and be able to compute them for various events

    • 6.Explain what a probability distribution is, and state its major use

    • 7.State the probabilities by using a binomial distribution

    • 8.Interpret the symbols in the binomial term

    Slide 25

    WHAT IS PROBABILITY

    • A.Applies exclusively to a future event

    • B.Probability statements are numeric, defined in the range 0 to 1, never more and never less

    • C.Defined as the ratio of the number of ways the specified event can occur to the total number of equally likely events that can occur

    • D.P(E), can be defined as the proportion of times a favorable event will occur in a long series of repeated trials:

    • P(E) = =

    =

    Slide 26

    Probability

    • Probabilities are always stated in terms between 1 and 0. (Rule 1)

    • The sum of all probabilities must equal 100%

    • Three basic rules

      • The independence rule

      • The product rule

      • The addition rule

    Slide 27

    Mutually exclusive

    Event A is mutually exclusive of event B, if event A occurs, than event B cannot occur and conversely, if event B occurs, than event A cannot occur.

    Rule 2: P(A) = 1 – P(A)

    Slide 28

    A

    A

    A

    A

    A

    A

    A

    A

    Independence

    • The first law of probability: (rule 5)

      • The probability of the occurrence of any two or more mutually exclusive events is equal to the sum of their separate probabilities.

    • If a coin is flipped two times in a row and heads shows on the first flip is there a better chance that heads will show on the second flip?

    • P{H|H}= {H|T}

    Coin 1 Coin 2

    H H

    H T

    T H

    T T

    Slide 29

    COMPLEMENTARY EVENTS

    • A.Event is the complement of event A

    • B.P(A) = sum of probabilities of outcomes in A

    • C.P = sum of probabilities of outcomes in

    • And

    • Therefore

    Slide 30

    A

    A

    A

    A

    A

    A

    A

    A not B

    AB

    (48 cards)

    (4 cards)

    Total 52 cards

    Slide 31

    Conditional Probability

    P (B | A) = P (AB) / P(A)

    The vertical line “|” between B and A indicates that the event to the right of the vertical line (event A) is a condition potentially influencing the probability of the event to the left (event B)

    • Suppose that in the case of drawing cards, the first card drawn is not replaced.

    • If the first card draw is an ace what is the probability that the second card with be an ace?

    • What is the probability the second will be a deuce?

    Slide 32

    • Probability of a Ace on first draw

      4/52 = 1/13 = .0769

    Slide 33

    A

    A

    A

    A

    A

    A

    A

    A

    A

    A not B

    AB

    (48 cards)

    (4 cards)

    Total 52 cards

    Slide 34

    • Probability of a Ace on first draw

      4/52 = 1/13 = .0769

    • Probability of an ace on second draw without replacement

      3/51 = .0588

    Slide 35

    A

    A

    A

    A

    A

    A

    A

    A

    A

    A not B

    AB

    (48 cards)

    (3 cards)

    Total 51 cards

    Slide 36

    • Probability of a Ace on first draw

      4/52 = 1/13 = .0769

    • Probability of an ace on second draw without replacement

      3/51 = .0588

    • Probability of a deuce on second draws without replacement

      4/51 = .0788

    Slide 37

    A

    2

    2

    2

    2

    2

    2

    2

    2

    A

    A

    A not B

    AB

    (48 cards)

    (4 cards)

    Total 51 cards

    Slide 38

    A

    A

    A

    A

    A

    A

    Product(Multiplication Theorem)

    P(AB) = P(A) x P(B | A)

    • The second law of probability: rule 6

      • The probability of the simultaneous or successive occurrence of two or more independent events is equal to the product of their separate probabilities.

    • What is the probability of drawing an ace three times in a row with replacement?

    1/13

    x 1/13

    x 1/13

    = 1/2197

    Slide 39

    Bayes’ Rule

    Conditional probability:

    P (B | A) = P (AB) / P(A)

    and

    Multiplication theorem

    P (AB) = P(A | B) P (B)

    Bayes’ Rules

    P (B | A) = P (A | B) P(B) / P(A)

    Slide 40

    Addition

    • All the different possible probabilities must add up to 1.0 (100%)

    • A probability must always be < 1.0

    • p + (1 – p) = 1.0

    • P(A or B) = P (A) + P (B) – P(AB)

    Slide 41

    Addition Theorem

    A

    B

    not

    A B

    A not B

    AB

    Total 52 cards

    Slide 42

    A

    A

    B

    not

    A B

    A not B

    AB

    AB

    B

    not

    A B

    A not B

    AB

    Total 52 cards

    Addition Theorem

    Total 52 cards

    The probability of drawing an ace or a club.

    P(A or B) = P (A) + P (B) – P(AB)

    P ( Ace or Club) = 4/52 + 13/52 – 1/52 = 16/52

    Slide 43

    PROBABILITY RULES

    • B.The Addition Rule:

    • The probability that event A or event B (or both) will occur equals the sum of the probabilities of each individual event minus the probability of both

    Slide 44

    COUNTING RULES

    • A.Rule 1: Number of Ways

      If event A can occur in n1, distinct ways and event B can occur in n2 ways,

      then the events consisting of A and B can occur in n1 – n2 ways

    • B.Rule 2: Permutations

      • 1.In determining the number of ways in which you can arrange a group of objects, you must first know whether the order of arrangement plays a role.

      • 2.A permutation is a selection of r objects from a group of n objects, taking the order of selection into account

    • C.Rule 3: Combinations

    • A selection of a subgroup of distinct objects, with order not being important

    Slide 45

    PROBABILITY DISTRIBUTIONS

    • A.Defined as a complete list of all possible outcomes, together with the probability of each

    • B.Random Variable – variable which can assume any number of values

    • C.A probability distribution is a list of the probabilities associated with the values of the random variable obtained in an experiment

    Slide 46

    LOTTERY PROBABILITY AND SAMPLING

    • A.Sampling without replacement –

      • once a number has been drawn (of subject selected) that number may not be drawn again

  • B.Sampling with replacement –

    • The same number (or subject) may be picked multiple times

  • Slide 47

    BINOMIAL DISTRIBUTION

    • A.A binomial distribution serves as a model for outcomes limited to two choices (e.g. sick or well, dead or alive…)

    • B.Expansion of the binomial term , where

      • 1. is the probability of successful outcome

      • 2. = 1 - is the probability of an unsuccessful outcome and

      • 3. is the number of trials or attempts

    Slide 48

    BINOMIAL DISTRIBUTION

    • C. The binomial expansion is applicable provided that

      • 1.Each trial has only two possible outcomes – success or failure

      • 2.The outcome of each trial is independent of the outcomes of any other trial

      • 3.The probability of success, pm is constant from trial to trial

    Slide 49

    1/4

    1/4

    1/2

    1/4

    1/4

    4/4 = 1

    Binomial expansion

    If two coins are tossed at once,

    four possibilities may occur.

    Coin 1 Coin 2

    H H

    H T

    T H

    T T

    Slide 50

    Binomial expansion

    If three coins are tossed at once,

    eight possibilities may occur.

    Slide 51

    BINOMIAL DISTRIBUTION

    • D.In general, the probability of an event consisting of r successes out of n trials is

      • where

        n = the number of trials in an experiment

        r = the number of successes

        n- r = the number of failures

        p = the probability of success

        q = 1 – p, the probability of failure

    Slide 52

    Factorials

    Slide 53

    BINOMIAL DISTRIBUTION

    • The expression

    • is a term from the binomial expansion

    Slide 54

    BINOMIAL DISTRIBUTION

    • The entire expansion lists the terms for r successes and (n – r) failures from the binomial distribution

      =

      =

    Slide 55

    Six coins?

    A table would be complex.

    Formula?

    (p + q)2 = pn + n/1 p(n-1)q + n(n-1)/(1)(2) p (n-2) q2 + …+ qn

    There is an easier way!

    (Pascal’s Triangle)

    Slide 56

    Pascal’s Triangle

    n Binomial Coefficient Denominator of p

    To complete 7 trials add a column on each end of the row insert the value 1 in those cells then add two adjacent numbers in line 6 and place that value

    In the cell on line 7 between the two values in the row below.

    Slide 57

    PowerBall Lottery

    53 White balls

    5 balls selected

    (53!)/(5!)(48!)=1/2,869,685

    42 red balls

    Powerball selected

    1/47

    Jackpot: 1/2,869,685* 1/47

    = 1/120,526,770

    Slide 58

    CONCLUSION

    Probability measures the likelihood that a particular event will or will not occur. In a long series of trials, probability is the ration of the number of favorable outcomes to the total number of equally likely outcomes. Permutations and combinations are useful in determining the number of outcomes. If compound events are involved, we need to select and apply the addition rule or the multiplication rule to compute probabilities. The outcome of an experiment together with its respective probabilities constitutes a probability distribution. One very common probability distribution is the binomial distribution. It presents the probabilities of various numbers of successes in trials where there are only two possible outcomes to each trial.


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