MATH 1342 Name
___________________________
Elementary Statistical Methods
Lab Ch.3 – Descriptive Statistics and the Shape of Distributions
Critical Thinking; Communication Skills; Empirical/Quantitative Skills
1. During our semester in statistics, we will be studying inferential statistics. In chapter 1,
we learned the basic premises behind inferential statistics. Write a sentence or two
summarizing inferential statistics in your own words.
The process of applying sampled data to infer or forecast characteristics of a larger sample data
set or population is known as inferential statistics. The several methods that statistics obtained from
observations on samples from research populations may be utilized to infer whether or not such
populations are genuinely distinct from one another are referred to as inferential statistics.
2. The inferential statistics that we will cover in our class will all be parametric statistics.
Parametric statistics require that certain requirements about the distribution must be met by
the sample data values. We have learned some different distributions in chapter 2. Sketch
and name the FOUR different distributions that we have learned.
Normal distribution - the normal distribution, often
referred to as the Gaussian distribution, is a symmetric probability
distribution about the mean that indicates that data close to the
mean occur more frequently than data distant from the mean. The
normal distribution is represented graphically as a "bell curve".
Binomial distribution A statistical probability distribution
known as a binomial distribution indicates the possibility that a given
value would take one of two independent values under specific
conditions or presumptions.
, MATH 1342 Name
___________________________
Elementary Statistical Methods
Lab Ch.3 – Descriptive Statistics and the Shape of Distributions
Critical Thinking; Communication Skills; Empirical/Quantitative Skills
Exponential distribution the probability density function is
plotted on an exponential distribution graph, which displays the
distribution of the time or distance between events. Lambda (λ) and
x are the two terms utilized in the exponential distribution graph. In
this case, x stands for time, and lambda for the number of
occurrences per unit of time.
Poisson distribution The Poisson distribution is
a type of discrete probability distribution that represents
the likelihood of a specific number of events taking place
within a predetermined time frame, provided that these
events occur at a constant mean rate and without regard
to the elapsed time since the last occurrence.
3. We have also learned that the mean and the median can give us hints about the
distribution of our data. Explain how the different distributions typically affect the mean and
the median. Be certain to include the words “resistant” and “non-resistant” in your
explanation.
In a normal distribution, the mean and median are equal. Both are at the center of the
distribution. This is the average value when the data is sorted. Because the normal distribution is
symmetrical, the median line is also in the center. The mean and median are resistant to outliers in a
normal distribution.
The mean of the binomial distribution is affected by the probability of success and the number
of trials. It is not always located at the distribution center.
___________________________
Elementary Statistical Methods
Lab Ch.3 – Descriptive Statistics and the Shape of Distributions
Critical Thinking; Communication Skills; Empirical/Quantitative Skills
1. During our semester in statistics, we will be studying inferential statistics. In chapter 1,
we learned the basic premises behind inferential statistics. Write a sentence or two
summarizing inferential statistics in your own words.
The process of applying sampled data to infer or forecast characteristics of a larger sample data
set or population is known as inferential statistics. The several methods that statistics obtained from
observations on samples from research populations may be utilized to infer whether or not such
populations are genuinely distinct from one another are referred to as inferential statistics.
2. The inferential statistics that we will cover in our class will all be parametric statistics.
Parametric statistics require that certain requirements about the distribution must be met by
the sample data values. We have learned some different distributions in chapter 2. Sketch
and name the FOUR different distributions that we have learned.
Normal distribution - the normal distribution, often
referred to as the Gaussian distribution, is a symmetric probability
distribution about the mean that indicates that data close to the
mean occur more frequently than data distant from the mean. The
normal distribution is represented graphically as a "bell curve".
Binomial distribution A statistical probability distribution
known as a binomial distribution indicates the possibility that a given
value would take one of two independent values under specific
conditions or presumptions.
, MATH 1342 Name
___________________________
Elementary Statistical Methods
Lab Ch.3 – Descriptive Statistics and the Shape of Distributions
Critical Thinking; Communication Skills; Empirical/Quantitative Skills
Exponential distribution the probability density function is
plotted on an exponential distribution graph, which displays the
distribution of the time or distance between events. Lambda (λ) and
x are the two terms utilized in the exponential distribution graph. In
this case, x stands for time, and lambda for the number of
occurrences per unit of time.
Poisson distribution The Poisson distribution is
a type of discrete probability distribution that represents
the likelihood of a specific number of events taking place
within a predetermined time frame, provided that these
events occur at a constant mean rate and without regard
to the elapsed time since the last occurrence.
3. We have also learned that the mean and the median can give us hints about the
distribution of our data. Explain how the different distributions typically affect the mean and
the median. Be certain to include the words “resistant” and “non-resistant” in your
explanation.
In a normal distribution, the mean and median are equal. Both are at the center of the
distribution. This is the average value when the data is sorted. Because the normal distribution is
symmetrical, the median line is also in the center. The mean and median are resistant to outliers in a
normal distribution.
The mean of the binomial distribution is affected by the probability of success and the number
of trials. It is not always located at the distribution center.
