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Problem : Assume that among diabetics the fasting blood level of glucose is approximately normally distributed with a mean of 105mg per 100ml and an SD of 9 mg per 100 ml. What proportion of diabetics having fasting blood glucose levels between 90 and 125 mg per 100 ml ?.

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Problem:

Assume that among diabetics the fasting blood level of glucose is approximately normally distributed with a mean of 105mg per 100ml and an SD of 9 mg per 100 ml. What proportion of diabetics having fasting blood glucose levels between 90 and 125 mg per 100 ml ?



INTRODUCTION

Statistically, a population is the set of all possible values of a variable.

Random selection of objects of the population makes the variable a random variable ( it involves chance mechanism)

Example: Let ‘x’ be the weight of a newly born baby. ‘x’ is a random variable representing the weight of the baby.

The weight of a particular baby is not known until he/she is born.


Discrete random variable:

If a random variable can only take values that are whole numbers, it is called a discrete random variable.

Example: No. of daily admissions

No. of boys in a family of 5

No. of smokers in a group of 100

persons.

Continuous random variable:

If a random variable can take any value, it is called a continuous random variable.

Example: Weight, Height, Age & BP.


Continuous probability distributions
Continuous Probability Distributions

  • Continuous distribution has an infinite number of values between any two values assumed by the continuous variable

  • As with other probability distributions, the total area under the curve equals 1

  • Relative frequency (probability) of occurrence of values between any two points on the x-axis is equal to the total area bounded by the curve, the x-axis, and perpendicular lines erected at the two points on the x-axis


Histogram
Histogram

Figure 1 Histogram of ages of 60 subjects


The Normal or Gaussian distribution is the most important continuous probability distribution in statistics.

The term “Gaussian” refers to ‘Carl Freidrich Gauss’ who develop this distribution.

The word ‘normal’ here does not mean ‘ordinary’ or ‘common’ nor does it mean ‘disease-free’.

It simply means that the distribution conforms to a certain formula and shape.


Histograms
Histograms continuous probability distribution in statistics.

  • A kind of bar or line chart

    • Values on the x-axis (horizontal)

    • Numbers on the y-axis (vertical)

  • Normal distribution is defined by a particular shape

    • Symmetrical

    • Bell-shaped


A perfect normal distribution
A Perfect Normal Distribution continuous probability distribution in statistics.


Gaussian distribution
Gaussian Distribution continuous probability distribution in statistics.

  • Many biologic variables follow this pattern

    • Hemoglobin, Cholesterol, Serum Electrolytes, Blood pressures, age, weight, height

  • One can use this information to define what is normal and what is extreme

  • In clinical medicine 95% or 2 Standard deviations around the mean is normal

    • Clinically, 5% of “normal” individuals are labeled as extreme/abnormal

      • We just accept this and move on.


The normal distribution

The Normal Distribution continuous probability distribution in statistics.


Characteristics of normal distribution
Characteristics of Normal Distribution continuous probability distribution in statistics.

  • Symmetrical about mean, 

  • Mean, median, and mode are equal

  • Total area under the curve above the x-axis is one square unit

  • 1 standard deviation on both sides of the mean includes approximately 68% of the total area

    • 2 standard deviations includes approximately 95%

    • 3 standard deviations includes approximately 99%


Characteristics of the normal distribution
Characteristics of the Normal Distribution continuous probability distribution in statistics.

  • Normal distribution is completely determined by the parameters  and 

    • Different values of  shift the distribution along the x-axis

    • Different values of  determine degree of flatness or peakedness of the graph


Applications of normal distribution
Applications of Normal Distribution continuous probability distribution in statistics.

  • Frequently, data are normally distributed

    • Essential for some statistical procedures

    • If not, possible to transform to a more normal form

  • Approximations for other distributions

  • Because of the frequent occurrence of the normal distribution in nature, much statistical theory has been developed for it.


What s so great about the normal distribution
What’s so Great about the Normal Distribution continuous probability distribution in statistics.?

  • If you know two things, you know everything about the distribution

    • Mean

    • Standard deviation

  • You know the probability of any value arising


Standardised scores
Standardised Scores continuous probability distribution in statistics.

  • My diastolic blood pressure is 100

    • So what ?

  • Normal is 90 (for my age and sex)

    • Mine is high

      • But how much high?

  • Express it in standardised scores

    • How many SDs above the mean is that?


  • Mean = 90, SD = 4 (my age and sex) continuous probability distribution in statistics.

  • This is a standardised score, or z-score

  • Can consult tables (or computer)

    • See how often this high (or higher) score occur

    • 99.38% of people have lower scores


Standard scores
Standard Scores continuous probability distribution in statistics.

  • The Z score makes it possible, under some circumstances, to compare scores that originally had different units of measurement.


Z score
Z score continuous probability distribution in statistics.

  • Allows you to describe a particular score in terms of where it fits into the overall group of scores.

    • Whether it is above or below the average and how much it is above or below the average.

  • A standard score that states the position of a score in relation to the mean of the distribution, using the standard deviation as the unit of measurement.

    • The number of standard deviations a score is above or below a mean.


Z score1
Z Score continuous probability distribution in statistics.

  • Suppose you scored a 60 on a numerical test and a 30 on a verbal test. On which test did you perform better?

    • First, we need to know how other people did on the same tests.

      • Suppose that the mean score on the numerical test was 50 and the mean score on the verbal test was 20.

      • You scored 10 points above the mean on each test.

      • Can you conclude that you did equally well on both tests?

      • You do not know, because you do not know if 10 points on the numerical test is the same as 10 points on the verbal test.


Z score2
Z Score continuous probability distribution in statistics.

  • Suppose you scored a 60 on a numerical test and a 30 on a verbal test. On which test did you perform better?

    • Suppose also that the standard deviation on the numerical test was 15 and the standard deviation on the verbal test was 5.

      • Now can you determine on which test you did better?


Z score3
Z Score continuous probability distribution in statistics.


Z score4
Z Score continuous probability distribution in statistics.


Z score5
Z score continuous probability distribution in statistics.

  • To find out how many standard deviations away from the mean a particular score is, use the Z formula:

    Population: Sample:


Z score6
Z Score continuous probability distribution in statistics.

In relation to the rest of the people who took the tests, you did better on the verbal test than the numerical test.


Properties of z scores
Properties of Z scores continuous probability distribution in statistics.

  • The standard deviation of any distribution expressed in Z scores is always one.

    • In calculating Z scores, the standard deviation of the raw scores is the unit of measurement.


Properties of z scores1
Properties of Z scores continuous probability distribution in statistics.

  • Transforming raw scores to Z scores changes the mean to 0 and the standard deviation to 1, but it does not change the shape of the distribution.

    • For each raw score we first subtract a constant (the mean) and then divide by a constant (the standard deviation).

    • The proportional relation that exists among the distances between the scores remains the same.

    • If the shape of the distribution was not normal before it was transformed, it will not be normal afterward.


The standard normal curve
The Standard Normal Curve continuous probability distribution in statistics.


The standard normal table
The Standard Normal Table continuous probability distribution in statistics.

  • Using the standard normal table, you can find the area under the curve that corresponds with certain scores.

  • The area under the curve is proportional to the frequency of scores.

  • The area under the curve gives the probability of that score occurring.


Standard normal table
Standard Normal Table continuous probability distribution in statistics.


Reading the z table

Finding the proportion of observations between the mean and a score when

Z = 1.80

Reading the Z Table


Reading the z table1

Finding the proportion of observations above a score when a score when

Z = 1.80

Reading the Z Table


Reading the z table2

Finding the proportion of observations between a score and the mean when

Z = -2.10

Reading the Z Table


Reading the z table3

Finding the proportion of observations below a score when the mean when

Z = -2.10

Reading the Z Table


Z scores and the normal distribution
Z scores and the Normal Distribution the mean when

  • Can answer a wide variety of questions about any normal distribution with a known mean and standard deviation.

  • Will address how to solve two main types of normal curve problems:

    • Finding a proportion given a score.

    • Finding a score given a proportion.


Finding proportions
Finding Proportions the mean when


Example finding a proportion below the mean
Example: Finding a Proportion Below the Mean the mean when

1.

2. Use C’ Column

3.


Example finding a proportion below the mean1
Example: Finding a Proportion Below the Mean the mean when

4.

1.

2. Use C’ Column

15.87 % of customers

3.



Example finding a proportion between the mean and a score1
Example: Finding a Proportion Between the Mean and a Score the mean when

4.

1.

2. Use B Column

3.

34.13% of the population


Example finding a proportion below a score
Example: Finding a Proportion Below a Score the mean when

Given that IQ is normally distributed with a mean of 100 and a standard deviation of 16, what proportion of the population has an IQ below 124?

3.

1.

2. 0.50 + B Column


Example finding a proportion below a score1
Example: Finding a Proportion Below a Score the mean when

Given that IQ is normally distributed with a mean of 100 and a standard deviation of 16, what proportion of the population has an IQ below 124?

4.

1.

.50 + .4332 = .9332

.9332 of the population

2. 0.50 + B Column

3.


Example finding a proportion between two scores
Example: Finding a Proportion Between Two Scores the mean when

3.

1.

2. B’ + B


Example finding a proportion between two scores1
Example: Finding a Proportion Between Two Scores the mean when

1.

4.

.4878+.4970=.9848

98.48%

2. B’ + B

3.


Example finding a proportion between two scores2
Example: Finding a Proportion Between Two Scores the mean when

Given that scores on the Social Adjustment Scale are normally distributed with a mean of 50 and a standard deviation of 10, what proportion of scores are between 30 and 40?

1.

3.

30

40

2. Larger C’-Smaller C’


Example finding a proportion between two scores3
Example: Finding a Proportion Between Two Scores the mean when

4.

Given that scores on the Social Adjustment Scale are normally distributed with a mean of 50 and a standard deviation of 10, what proportion of scores are between 30 and 40?

.1587-.0228 =.1359

1.

30

40

2. Larger C’-Smaller C’

3.


Finding scores
Finding Scores the mean when


Example finding a score from a proportion or percentile rank
Example: Finding a Score from a Proportion (or Percentile Rank)

3.

Given that scores on the Social Adjustment Scale are normally distributed with a mean of 50 and a standard deviation of 10, what two scores correspond to the middle 95%?

1.

4.

2. 0.025 in C and C’


Example finding a score from a proportion or percentile rank1
Example: Finding a Score from a Proportion (or Percentile Rank)

Given that IQ scores are normally distributed with a mean of 100 and a standard deviation of 16, what score corresponds to the 95th percentile?

3.

1.

4.

2. 0.50 + 0.45 (B Column)


Normal distributions go wrong
Normal Distributions Go Wrong Rank)

  • Wrong shape

    • Non-symmetrical

      • Skew

    • Too fat or too narrow

      • Kurtosis

  • Aberrant values

    • Outliers


Effects of non normality
Effects of Non-Normality Rank)

  • Skew

    • Bias parameter estimates

      • E.g. mean

  • Kurtosis

    • Doesn’t effect parameter estimates

      • Does effect standard errors

  • Outliers

    • Depends


Distributions
Distributions Rank)

  • Bell-Shaped (also known as symmetric” or “normal”)

  • Skewed:

    • positively (skewed to the right) – it tails off toward larger values

    • negatively (skewed to the left) – it tails off toward smaller values


Kurtosis
Kurtosis Rank)


Outliers
Outliers Rank)


Dealing with outliers
Dealing with Outliers Rank)

  • Error

    • Data entry error

    • Correct it

  • Real value

    • Difficult

    • Delete it


ANY Rank)

QUESTIONS


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