# Probability and Statistics: A Comprehensive Overview

## 1.V

The binomial **model** is characterized by dichotomous. (There – FAILURE)

## 2.F

The **normal** curve is asymmetrical with respect to the mean. (DEPENDS definition)

## 3.F

Probability theory works with deterministic experiments.

(RAND PROBABILISTIC).

### 4x

Probability theory allows to get to build a model of the **random** **experiment**.

## 5.F

The route of the standard model is defined on positive real numbers. (Negative-positive)

## 6.V

Decisions based on **probability** theory are positive.

## 7.F

A random experiment is the process of gathering information for an **event** that shows a result when repeated several times. (Samples at random)

Probability theory **8.V** delivery procedures for calculating the results of a randomized trial. **9.F** in a randomized experiment can define a set of possible outcomes.

## 10.F

The set of possible outcomes of the random experiment recognized by random experiment. **11.F** binomial model events are dependent (INDEPENDENT).

## 12.V

If the experiment is to throw three coins, the number of possible outcomes will be 8.

## 13.V

An event is a subset and a **sample** **space**.**14.V** the empty set is known as an event emptied.

## 15.V

If the **probability** of an event approaches one then the event is very likely to happen.

## 16.F

The sample space is known as an event likely.

## 17.V

A probability can be defined as: frecuentita and subjective.

## 18.V

A Bayesian probability is the degree of certainty people have about an event.

## 19.F

The opposite is the event that consists of all elements **20.V** placed with the sample space.

## 21.F

A sample space is finite and countable if it has a finite number of terms and these and these belong to the real numbers (NOT JUST THE ACTUAL)

## 22.V

The probability of the sample space is equal to one half (or failure).

## 23.F

The probability of a subset is the relative size in total.

The probability of n **24.V** subset is the relative size in total.

## 25.V

Let A c E such that P (A) = 1-P (A)

## 26.F

Let A1, A2 c E such that any events: P (A1-A2) = P (A1UA2)

## 27.V

The probability that event A occurs is calculated by P (A) = # A / # E

## 28.V

The union is the event which consists of the experimental results that are in A or B or both.

## 29.V

To calculate a conditional probability is to calculate the intersection between two events.

## 30.V

In the Gaussian model is bell-shaped curve. **31.F** subjective probability of an event is the relative frequency of times that the event would happen to run an experiment again and again.

## 32.F

A system is comprehensive and inclusive if the union of events is a sample space and their interaction is different vacuum

## 33.V

Two events are independent if one occurs, it adds information on the other.

## 34.V

Bayes theorem calculates a conditional probability of an event Ai (i = 1,2, …, n) of the partition of the sample space conditional on event B.

## 35.F

The specificity is determined by the probabilities of the true positives.

## 36.V

The prevalence is the percentage of the population who has an illness.

## 37.V

P (Ill / +) = positive predictive index.

## 38.V

A random **variable** is a function which assigns each event a number. **39.F** **random variables** can be described as discrete and discontinuous.

## 40.V

A density function is a nonnegative function of area equal to one.

## 41.F

The incidence is the percentage of cases of disease present in a population.

## 42.V

In the probability density functions of an interval describes a certain area.

## 43.F

The expected value equals the median.

## 44.F

The sensitivity is determined by the probabilities of the true negatives.

### 45f

The parameters of a normal model are: the mean and proportion.

## 46.V

In a normal average model factor is the location of the curve.

## 47.V

Kart Gauss determined the normal pattern through stellar observations.

## 48.F

P (Ill / -) = negative predictive index. **49.V** normal model delivers the standard deviation curve shape.

## 50.F

The success of this intersection is composed of the elements that are in A or B.

## 1.V

SPSS normality test indicates that Ho: variable is normal and that H1: the variable is not normal.

**2x** the normal model is determined by the mean and desviació? Nt? Pike.

## 3.V

In Gauss is known that between half and one desviació? Nt? Pike likely have about 68%.

### 4x

The funci? N normal density is sim? Trica.

### 5x

To estimate the pair? Meters from the linear regression model? N using the so called for estimates? Nm? Nimo picture? Tica errors.

The **6.V** normal with mean 0 and desviació? Nt? Pica 1 is known as standard normal.

## 7.V

For normal variable X, the Interpretaci? N is: Assign to every value of N (and, or) a value of N (0,1) that leaves exactly the same probability below.

## 8.V

The average in the normal model is a translational factor? N.

The desviació **9.V?**

Nt? Pike in the normal pattern determines the shape of the curve.

## 10.V

If P (Z <1.85) = 0.068 then P (Z> 1.85) = 0.032. **11.** The probability P (Z <0) = 0.25 **12.** Although not a random variable has a distribution? N normal, certain states? Stico / estimators calculated on large random samples, if possess? N a distribution? N normal. **13.** The desviació? Nt? Pica always be average? Equivalent to desviació? Nt? Pica normal variable. **14.** The average of a random sample that comes in a population? N be normal anyway? Normal. **15.F** With tipificaci? No one can compare different measurements of normal models. **16.V** If N> (greater) 20, the average be? Normal.

## 17.F

The Chi-square model is sim? Trich.

## 18.F

Student T model is sim? Igniter on average.

## 19.V

Gaussian model appears in the appearances of measurement errors. **20.** If n> 30 and p sin? Or (np> 5), n large then the model can approximate the Poisson curve.

## 21.V

F snededor model has two pair? Meters.

The population **22.V?**

N ideal for investigation is called poblaci? No goal.

## 23.V

Chi-square test applied to verify the independence or insert variables into scales raz? N.

## 24.F

The group that we can actually study called poblaci? No target. **25.V** sampling probabil? Sticos know the probability that an individual is elected to the sample.

Sampling **26.F** probabil not? Stico no bias. **27.** The t? Cnicas inference estad? Stica assume that the sample was selected using more

## 28.V

To avoid such biases we used the t? Cnicas random response.

## 29.V

The cluster sampling is applied when it’s hard to have a list of all individuals who are part of the poblaci? No study.

## 30.F

An estimator is a sum num? Rich calculated on a sample and it is a good representational? No one state? Stico.

## 31.F

The desviació? Nt? Pike from the sample mean is o / n. **32.** The bias due to different systems? Cies between poblaci? No objective and poblaci? No study is called The selection bias? N. **33.** The Estimate? No confidence interval given a set of estimates and probability of error. **34.** The inference estad? Stica is the set of m? All of which lead to character? Sticas from a sample probability? Stica. **35.** A hip? Thesis estad? Stica is a procedure to establish a decisive? N with respect to random variables est? N present a probability model.

## 36.V

The covariance measures the strength of relaci? N.

## 37.V

The Pearson r coefficient est? Between 1 and -1.

## 38.V

The coefficient r is dimensionless.

The **39.V** an? Analysis of regression? N used to predict the relaci? N of the dependent variable in funci? No independent variable.

## 40.F

In the hip? Thesis H1: the data can refute it.

## 41.F

In the hip? Thesis H0: no duty? To be accepted with strong evidence in favor.

## 42.V

The confidence level is 90%, then the probability of error is 0.10.

## 43.V

The coefficient r square interpretation, the percentage of variability of the independent variable.

The graphic Dispersió **44.V?**

N, is a graph of Gauss, which measure the tendency of the data.

### 45f

If p> H1 Alpha is rejected.

## 46.V

The contrast is not significant, when p> alpha.

## 47.V

The error type two, says, H0 is accepted as this is false **48.** The Mann Whitney test is a test to compare the means of two related samples.

## 49.F

The p-value is known before the experiment. **50.** The Wilxcon test, a test is not param? Trica to get the means of two related samples.