1. What is the branch of mathematics that deals with the colecion, organization, presentation, analysis, and irterpretation of data? A. Aigebra 2 Which of the folowing is a complete colection of al elements to be studec? B. Geomety C. Satistics D. Trigonomety C. Sample D. Statisics B. Popuation 3. Which of the following is a subset of a population that wil serve as a representative of the whole populaton? B. Popuation A Census A Census C. Sample D. Statistics _4. Which of the folowing situations can stafstics NOT be apple? A Descrbing the scores of the students. C. Reading educational books for iteracy. B. Collecing and analyzing data from experiments. D. Identlying the favorte pass time of students. _5. Which of the folowing nature of data can be characterized by numbers representing courts or measurements? B. Discrete _6. What is the nature of data that can be dstinguished by some nonumeric characderistics? B. Discrete 7. quantatve data takes on numerical value that can be measured on a numerical scale, which of the folowing is NOTa A Continuous C. Qualtative D. Quanttatve A Continuous C. Qualtative D. Quanttative quanttative data? A Age B. Height C. Weight D. Rank &. Which of the following data results from ether a finite number of possible values or countable number of possible values as 0, or 1, or 2, and so on? A. Continuous 3. Which of the folowing data resut from infntely many possble values hat can be associated with points on a contiruous scale in such a way that there are no gaps or intemuptions? B. Discrete C. Qualtlative D. Quantative B. Discrete C. Ouaitative A Continuous D. Quanttative _10. What level of measurementis characterized by data that consist of names, labels, or categories only? A Interval B. Nominal C. Ordnal D. Ratio _1. Which of the following is TRUE about qualitative data? A tcan be nominal. B. tcan be ratio. C. It can be interval. D. R can be dscrete. 12 Qualitative is to categorical as to Quantative is to A Continuous B. Discrete C. Nominal D. Numerical
Addition Rule of Probability
It simply refers to the likelihood of an event taking place whenever the occurrence of an event is uncertain. The probability of a single event can be calculated by dividing the number of successful trials of that event by the total number of trials.
Expected Value
When a large number of trials are performed for any random variable ‘X’, the predicted result is most likely the mean of all the outcomes for the random variable and it is known as expected value also known as expectation. The expected value, also known as the expectation, is denoted by: E(X).
Probability Distributions
Understanding probability is necessary to know the probability distributions. In statistics, probability is how the uncertainty of an event is measured. This event can be anything. The most common examples include tossing a coin, rolling a die, or choosing a card. Each of these events has multiple possibilities. Every such possibility is measured with the help of probability. To be more precise, the probability is used for calculating the occurrence of events that may or may not happen. Probability does not give sure results. Unless the probability of any event is 1, the different outcomes may or may not happen in real life, regardless of how less or how more their probability is.
Basic Probability
The simple definition of probability it is a chance of the occurrence of an event. It is defined in numerical form and the probability value is between 0 to 1. The probability value 0 indicates that there is no chance of that event occurring and the probability value 1 indicates that the event will occur. Sum of the probability value must be 1. The probability value is never a negative number. If it happens, then recheck the calculation.
answer the multiple choice and the performance task no.1 and 2
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