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| 8 Theory slides |
| 7 Exercises - Grade E - A |
| Each lesson is meant to take 1-2 classroom sessions |
Here are a few recommended readings before getting started with this lesson.
In a multiple-choice test, Davontay randomly selected the answers to all five questions. Each question had two options to choose from.
To find out about the voting behavior of people according to their age, a survey was conducted outside of a polling station. The following table shows some data about the presidential election in 2016 between Donald Trump and Hillary Clinton.
Clinton | Trump | |
---|---|---|
Young Adults (18−29years old) | 55 | 36 |
Adults (30−44years old) | 51 | 41 |
Middle-Aged Adults (45−64years old) | 44 | 52 |
Seniors (65+years old) | 45 | 52 |
Find the following conditional probabilities and describe their meaning in everyday words. Round each answer to two decimal places.
Descriptions:
Alternative description: If a person is selected at random among the surveyed young adults, there is a 60% chance they voted for Clinton and a 40% chance they voted for Trump.
Descriptions:
Alternative description: If a person that voted for Clinton is selected at random, there is a 26% chance that they are aged between 30 and 44 years old and a 23% chance that they are aged between 45 and 64.
Descriptions:
Descriptions:
Clinton | Trump | Total | |
---|---|---|---|
Young Adults (18−29years old) | 55 | 36 | 91 |
Adults (30−44years old) | 51 | 41 | 92 |
Middle-Aged Adults (45−64years old) | 44 | 52 | 96 |
Seniors (65+years old) | 45 | 52 | 97 |
Total | 195 | 181 | 376 |
In other words, if a person is selected at random among the surveyed young adults, there is a 60% chance they voted for Clinton and a 40% chance they voted for Trump.
In other words, if a person that voted for Clinton is selected at random, there is a 26% probability that they are between 30 and 44 years old and a 23% probability that they are between 45 and 64.
Last month, Ignacio got a part-time job working from 4:00 P.M. to 8:00 P.M. during weekdays. Ignacio, who knows statistics, said to his peers that the events of taking a nap after lunch and being late for work are independent. However, Tadeo, who does not know much about statistics, does not understand what Ignacio meant.
Late | On Time | Total | |
---|---|---|---|
Nap | 2 | 6 | 8 |
No Nap | 3 | 9 | 12 |
Total | 5 | 15 | 20 |
The events of taking a nap after lunch and being late for work are independent.
Ignacio is saying that the probability that he is late for work is the same whether or not he takes a nap after lunch. Therefore, on the days that Ignacio is late for work, the nap is not the cause. With this explanation, Tadeo will hopefully understand what Ignacio meant.
Ignacio taking a nap after lunchand
Ignacio being late for workare independent using the data from the table. Remember, if two events A and B are independent, then P(A) is equal to P(A∣B). Therefore, the following equation needs to be checked.
In Maya's new neighborhood, some people have dogs, cats, both, or neither. The following diagram shows the distribution of pets, but Maya has not seen it.
Notice that as they are written, these probabilities represent the same situation. However, Maya knows that Magdalena does not have a cat and that Dylan does not have a dog. With this information, the above probabilities can be rewritten.
These are conditional probabilities. Since there are only two types of pets in the survey, the above statement can be written more precisely.
By the Complement Rule, the following pair of conclusions can also be drawn.
From the four statements, Maya could safely ask Magdalena about her dog, but she should not ask Dylan about his cat. Keep in mind that Magdalena might not have a dog despite the probabilities and conclusions. Similarly, Dylan might have a cat.
Tearrik wants to determine if there is a connection between age and music preference. To figure it out, he surveyed 120 people at the mall, asking their age and whether they prefer pop or classical music. After analyzing the data collected, he concluded that there is no connection at all.
Pop | Classical | Total | |
---|---|---|---|
35 Years Old or Younger | |||
Older Than 35 | 40 | ||
Total | 45 | 120 |
Based on the conclusion made by Tearrik, complete the missing information in the two-way frequency table.
Pop | Classical | Total | |
---|---|---|---|
35 Years Old or Younger | 30 | 50 | 80 |
Older Than 35 | 15 | 25 | 40 |
Total | 45 | 75 | 120 |
Since there is no connection between age and music preference, the probability that someone older than 35 likes pop music is the same as the probability that any person likes this type of music. In other words, the events A person likes pop music
and A person is older than 35 years old
are independent.
For simplicity, some variables will be assigned to the missing data.
Pop | Classical | Total | |
---|---|---|---|
35 Years Old or Younger | a | b | c |
Older Than 35 | d | e | 40 |
Total | 45 | f | 120 |
totalrow and column add to the grand total 120, the missing marginal frequencies can be calculated.
Pop | Classical | Total | |
---|---|---|---|
35 Years Old or Younger | a | b | 80 |
Older Than 35 | d | e | 40 |
Total | 45 | 75 | 120 |
To find the joint frequencies, the conclusion made by Tearrik will be used instead of a system of equations.
There is no connection between age and music preference.
Pop | Classical | Total | |
---|---|---|---|
35 Years Old or Younger | a | b | 80 |
Older Than 35 | 15 | e | 40 |
Total | 45 | 75 | 120 |
Pop | Classical | Total | |
---|---|---|---|
35 Years Old or Younger | 30 | b | 80 |
Older Than 35 | 15 | 25 | 40 |
Total | 45 | 75 | 120 |
Pop | Classical | Total | |
---|---|---|---|
35 Years Old or Younger | 30 | 50 | 80 |
Older Than 35 | 15 | 25 | 40 |
Total | 45 | 75 | 120 |
Note that the conclusion made by Tearrik implies that the following pairs of events are independent.
A person likes pop musicand
A person is older than 35 years old.
A person likes pop musicand
A person is 35 years old or younger.
A person likes classical musicand
A person is older than 35 years old.
A person likes classical musicand
A person is 35 years old or younger.
Diego wants to throw a party at the end of the school year. To determine what kind of treats he should buy, he asked his 80 classmates whether they prefer cupcakes, cookies, donuts, or chocolate.
On the day of the party, Diego puts the treats on a table.
Substitute values
Calculate quotient
Convert to percent
Round to nearest integer
Substitute values
Calculate quotient
Convert to percent
Round to nearest integer
Substitute values
Calculate quotient
Convert to percent
Round to nearest integer
Substitute values
Calculate quotient
Convert to percent
Round to nearest integer
Substitute values
Calculate quotient
Convert to percent
Round to nearest integer
Substitute values
ba=b/16a/16
Substitute values
ba=b/9a/9
Substitute values
ba=b/7a/7
Davontay took a multiple-choice test where each question had two choices. He randomly guessed the answers to all the five questions in the test.
Let A be the event of guessing the answer to the second question correctly. Let B be the event of guessing correctly on the fifth question.
The events being independent means that having guessed the second question correctly does not influence having guessed the fifth question correctly and vice versa.
When penalties are called in basketball, a player may be given one to three free throw attempts. During a basketball season, a statistician working for a pro team's analytics department made the following observations for instances where two free throws were awarded.
Let's make a two-way frequency table using the statistician's data set.
To determine if the probability of making the second free throw is dependent on whether the player made the first free throw, we will calculate the conditional probability of making the second free throw given that the player made the first free throw and given that the player missed the first free throw. $P(make second|made first)$ [0.4em] $P(make second|missed first)$ If these probabilities are different, we can argue that there may be a dependency.
Knowing the total number of observations when a player made or missed the first free throw allows us to calculate the conditional probabilities of making the second free throw. P (make second|made first )& = 51/71 ≈ 72 % [1em] P (make second|missed first )& = 25/39 ≈ 64 % As we can see, according to this data, a basketball player is more likely to make the second free throw if they made their first attempt (72 %) than if they missed it (64 %). Perhaps the hot-hand fallacy is not a fallacy after all? (All kidding aside, it is a fallacy.)
In the lot of a car dealership, 80% of all cars have five seats, 25% are sports cars, and 10% are both sports cars and have five seats.
We know that 25 % of the cars are sports cars. Therefore, 75 % must not be sports cars. Similarly, if 80 % have five seats, then 20 % of the cars must not have five seats. Let's make a two-way table and include all of these statistics.
Using the statistics in the two-way table, we can calculate some conditional probabilities. P(not five seats|sports car) [-1em] 25 %-10 %=15 % P(not sports car|five seats) [-1em] 80 %-10 %=70 % Let's add these probabilities to the diagram.
Now we can calculate the probability that a randomly selected car does not have five seats and is not a sports car. 20 % -15 % = 5 % ✓ We can also calculate the percentage of cars that are neither five seaters nor sports cars as the difference between the total percentage of non sports cars and the percentage of non sports cars that are five seaters. 75 % -70 % = 5 % Now we can complete our table.
We want to know the probability that a sports car does not have five seats. Therefore, we must divide the percentage of sports cars that do not have five seats by the total percentage of sports cars.
Now we can determine the conditional probability of a car not having five seats given that it is a sports car. P(not five seats|sports car)&=0.15/0.25 [0.15cm] &=60 %
If sports cars are associated with not having five seats, the probability of a sports car not having five seats must be greater than the general probability that any given car does not have five seats. P(not five seats for all cars)&=20 % [0.5em] P(not five seats|sports car)&=60 % As we can see, the events are associated because the probability that a sports car does not have five seats is greater than the overall probability of a car not having five seats.