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UM / Electrical Engineering / EENG 311 / What is the definition of descriptive statistics?

What is the definition of descriptive statistics?

What is the definition of descriptive statistics?

Description

School: University of Miami
Department: Electrical Engineering
Course: Applied Probability and Statistics
Professor: Francesco travascio
Term: Fall 2015
Tags: Statistics
Cost: 50
Name: IEN 311 Exam #1 Study Guide
Description: Guide of everything you need to know for the first exam. With detailed notes and examples.
Uploaded: 02/05/2016
14 Pages 39 Views 1 Unlocks
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"}ul.lst-kix_4ksj7k9rbg3g-7{list-style-type:none}ul.lst-kix_4ksj7k9rbg3g-8{list-style-type:none}.lst-kix_j4ih81u4onp3-1 > li:before{content:"○ "}.lst-kix_e90kqvr75fh2-8 > li:before{content:"■ "}.lst-kix_7gxjkg1wyu98-1 > li:before{content:"○ "}ul.lst-kix_4ksj7k9rbg3g-5{list-style-type:none}.lst-kix_f4vbgrixw04v-7 > li:before{content:"○ "}.lst-kix_5d65t148u32r-8 > li{counter-increment:lst-ctn-kix_5d65t148u32r-8}ul.lst-kix_4ksj7k9rbg3g-6{list-style-type:none}.lst-kix_7gxjkg1wyu98-2 > li:before{content:"■ "}.lst-kix_j4ih81u4onp3-4 > li:before{content:"○ "}ul.lst-kix_4ksj7k9rbg3g-0{list-style-type:none}.lst-kix_e90kqvr75fh2-4 > li:before{content:"○ "}.lst-kix_7gxjkg1wyu98-5 > li:before{content:"■ "}.lst-kix_qelgc4reiuvl-0 > li:before{content:"- "}.lst-kix_7ctjcjgt83j3-5 > li:before{content:"■ "}ul.lst-kix_4ksj7k9rbg3g-3{list-style-type:none}ul.lst-kix_4ksj7k9rbg3g-4{list-style-type:none}.lst-kix_f4vbgrixw04v-8 > li:before{content:"■ "}ul.lst-kix_4ksj7k9rbg3g-1{list-style-type:none}ul.lst-kix_4ksj7k9rbg3g-2{list-style-type:none}.lst-kix_qelgc4reiuvl-3 > li:before{content:"- "}.lst-kix_qelgc4reiuvl-4 > li:before{content:"- "}.lst-kix_7ctjcjgt83j3-6 > li:before{content:"● "}ul.lst-kix_7ctjcjgt83j3-1{list-style-type:none}ul.lst-kix_7ctjcjgt83j3-2{list-style-type:none}.lst-kix_cxs8udvu7r-4 > li:before{content:"○ "}ul.lst-kix_7ctjcjgt83j3-0{list-style-type:none}.lst-kix_41rrla5ltj9r-0 > li:before{content:"● "}ul.lst-kix_7ctjcjgt83j3-5{list-style-type:none}ul.lst-kix_7ctjcjgt83j3-6{list-style-type:none}ul.lst-kix_7ctjcjgt83j3-3{list-style-type:none}ul.lst-kix_7ctjcjgt83j3-4{list-style-type:none}.lst-kix_1aa03xvqe3p6-8 > li:before{content:"" counter(lst-ctn-kix_1aa03xvqe3p6-8,lower-roman) ". "}.lst-kix_qmdw20vl8s5b-0 > li:before{content:"● "}.lst-kix_qmdw20vl8s5b-8 > li:before{content:"■ "}ul.lst-kix_ht5r67mcyf0w-0{list-style-type:none}.lst-kix_e90kqvr75fh2-0 > li:before{content:"● "}ul.lst-kix_ht5r67mcyf0w-3{list-style-type:none}ul.lst-kix_ht5r67mcyf0w-4{list-style-type:none}.lst-kix_5d65t148u32r-3 > li{counter-increment:lst-ctn-kix_5d65t148u32r-3}ul.lst-kix_41rrla5ltj9r-0{list-style-type:none}ul.lst-kix_ht5r67mcyf0w-1{list-style-type:none}ul.lst-kix_ht5r67mcyf0w-2{list-style-type:none}ol.lst-kix_5d65t148u32r-7.start{counter-reset:lst-ctn-kix_5d65t148u32r-7 0}ul.lst-kix_41rrla5ltj9r-2{list-style-type:none}ul.lst-kix_ht5r67mcyf0w-7{list-style-type:none}ul.lst-kix_41rrla5ltj9r-1{list-style-type:none}ul.lst-kix_ht5r67mcyf0w-8{list-style-type:none}ul.lst-kix_41rrla5ltj9r-4{list-style-type:none}.lst-kix_cxs8udvu7r-0 > li:before{content:"● "}ul.lst-kix_ht5r67mcyf0w-5{list-style-type:none}ul.lst-kix_41rrla5ltj9r-3{list-style-type:none}.lst-kix_41rrla5ltj9r-4 > li:before{content:"○ "}ul.lst-kix_ht5r67mcyf0w-6{list-style-type:none}ul.lst-kix_41rrla5ltj9r-6{list-style-type:none}ul.lst-kix_41rrla5ltj9r-5{list-style-type:none}.lst-kix_5d65t148u32r-5 > li:before{content:"(" counter(lst-ctn-kix_5d65t148u32r-5,lower-roman) ") "}ul.lst-kix_41rrla5ltj9r-8{list-style-type:none}.lst-kix_tivgw5ep6oul-7 > li:before{content:"○ "}.lst-kix_f4vbgrixw04v-4 > li:before{content:"○ "}ul.lst-kix_41rrla5ltj9r-7{list-style-type:none}.lst-kix_vpfido5tm1tx-2 > li:before{content:"■ "}.lst-kix_jo02mc47qhbs-6 > li:before{content:"● "}.lst-kix_4ksj7k9rbg3g-1 > li:before{content:"○ "}.lst-kix_vpfido5tm1tx-6 > li:before{content:"● "}.lst-kix_ht5r67mcyf0w-1 > li:before{content:"○ "}.lst-kix_5d65t148u32r-1 > li:before{content:"" counter(lst-ctn-kix_5d65t148u32r-1,lower-latin) ") "}.lst-kix_tivgw5ep6oul-3 > li:before{content:"● "}.lst-kix_f4vbgrixw04v-0 > li:before{content:"● "}.lst-kix_4ksj7k9rbg3g-5 > li:before{content:"■ "}.lst-kix_41rrla5ltj9r-8 > li:before{content:"■ "}.lst-kix_ht5r67mcyf0w-5 > li:before{content:"■ "}.lst-kix_jo02mc47qhbs-2 > li:before{content:"■ "}.lst-kix_qmdw20vl8s5b-4 > li:before{content:"○ "}.lst-kix_tv783q4ms68r-0 > li:before{content:"● "}.lst-kix_c4v1xvaqis7n-1 > li:before{content:"○ "}ul.lst-kix_o5eg4m5lpd8v-4{list-style-type:none}ul.lst-kix_o5eg4m5lpd8v-3{list-style-type:none}ol.lst-kix_5d65t148u32r-8.start{counter-reset:lst-ctn-kix_5d65t148u32r-8 0}ul.lst-kix_o5eg4m5lpd8v-6{list-style-type:none}ul.lst-kix_o5eg4m5lpd8v-5{list-style-type:none}ul.lst-kix_o5eg4m5lpd8v-0{list-style-type:none}ul.lst-kix_o5eg4m5lpd8v-2{list-style-type:none}ul.lst-kix_o5eg4m5lpd8v-1{list-style-type:none}.lst-kix_c4v1xvaqis7n-5 > li:before{content:"■ "}.lst-kix_o5eg4m5lpd8v-2 > li:before{content:"■ "}.lst-kix_tv783q4ms68r-4 > li:before{content:"○ "}.lst-kix_c9vx7bmur01g-5 > li:before{content:"■ "}ul.lst-kix_o5eg4m5lpd8v-8{list-style-type:none}ul.lst-kix_o5eg4m5lpd8v-7{list-style-type:none}.lst-kix_1aa03xvqe3p6-5 > li{counter-increment:lst-ctn-kix_1aa03xvqe3p6-5}.lst-kix_tv783q4ms68r-8 > li:before{content:"■ "}.lst-kix_1aa03xvqe3p6-0 > li:before{content:"" counter(lst-ctn-kix_1aa03xvqe3p6-0,decimal) ") "}.lst-kix_c9vx7bmur01g-1 > li:before{content:"○ "}ul.lst-kix_c4v1xvaqis7n-1{list-style-type:none}ul.lst-kix_c4v1xvaqis7n-0{list-style-type:none}.lst-kix_f8crvfra7muk-6 > li:before{content:"● "}ul.lst-kix_c4v1xvaqis7n-3{list-style-type:none}ul.lst-kix_c4v1xvaqis7n-2{list-style-type:none}ul.lst-kix_c4v1xvaqis7n-5{list-style-type:none}.lst-kix_qt14y0msu9qh-6 > li:before{content:"● "}ul.lst-kix_c4v1xvaqis7n-4{list-style-type:none}.lst-kix_1aa03xvqe3p6-4 > li:before{content:"(" counter(lst-ctn-kix_1aa03xvqe3p6-4,lower-latin) ") "}ul.lst-kix_7ctjcjgt83j3-7{list-style-type:none}ul.lst-kix_7ctjcjgt83j3-8{list-style-type:none}.lst-kix_qt14y0msu9qh-2 > li:before{content:"■ "}

Applied Probability and Statistics 1We also discuss several other topics like What is the purpose of child savers?

Chapter 1,2,3If you want to learn more check out What is the meaning of hue and cry in medieval england?

If you want to learn more check out Biology 211 Comprehensive Notes
We also discuss several other topics like What types of research designs are used in developmental research?

Descriptive Statistics

We also discuss several other topics like Why have cultures created music for so long?

Example:

Consider: 1.7, 2.2, 3.9, 3.11, 14.7

Calculate the mean and medianIf you want to learn more check out What happens in a competitive industry when permanent change in demand occurs?

  • Step 1: Add all the numbers up and divide by n (number of terms). The average is the mean: 1.7 + 2.2+ 3.9+3.11+14.7 = S. 122

                                        5

  • Step 2: Line the numbers up in order of least to greatest, the one in the middle is your median: 1.7, 2.2, 3.9, 3.11, 14.7

Ans: 3.9

The Trimmed Mean (Only  use in extreme measures)

  • Similar to the mean, but we trim the data
  • Eliminate very high/low data

  Example:

  Consider: 1.7, 2.2, 3.9, 3.11, 14.7, 0.1,2,3.5, 4.6, 5.7

  Calculate the 10% trimmed mean

  • There are 10 numbers (n=10)
  • 10% of 10 = 1 Trim highest and lowest values

  • Step 1: Line up the numbers in order from least to greatest and eliminate the highest and lowest values

            0.1, 1.7, 2, 3.5, 3.9, 3.11, 4.6, 5,7,14.7

            Your new set becomes: 1.7, 2, 3.5, 3.9, 3.11, 4.6, 5,7

  • Step 2: Calculate the mean of your new data

             1.7, 2, 3.5, 3.9, 3.11, 4.6, 5,7     = Xtr (10) = 3.34

                                7

Variance:

  • Indicates how spread the data are :

                                   

Standard Deviation

  • The positive square root of S2

Dot Plot

  • Use when you have a lot of data points

       Example: # of pets in family homes

       

Scatter Plot

  • Graph in which the valves of two different variables are plotted along two axis

           

       Usually shows correlation

Stem and Leaf Diagram

  • Diagram where data is arranged by place value. Especially useful for calculating mean and median

       Example:

       Data: 15,16,21,23,23,26,26,30,32,41

         

Box Plot

  • Graphical version of Statistical data focused on the minimum, first quartile, third quartile, median and maximum

 

 Example:

Probability

  • Numerical Data: (ex: strength of metal vs temp)
  • Categorical Data (ex: defective/non-defective items)

Sample Space:

  • The set of all the possible outcomes of an experiment

  • Each outcome is called an element/member/sample point

Example: What is the sample space for?

  1. Rolling a die → 1-6 = 6→ S {1,2,3,4,5,6}
  2. Tossing a coin → H orT = 2 → S= {1,2}
  3. Picking a card from a deck → {1-10,J,Q,K,A} = S

Tree Diagram

  • A. Diagram with a form of branching lines that are all connected and represent different possibilities and relationships. Identifies the outcomes.

       

            Example: Defective of Non Defective (N vs D)

       

Event

  • An event is simply a subset of a sample space

      Example:

  1. Rolling a die
  2. Event A in which they are even
  3. Event B in which they are divisible by 3

                                S={1,2,3,4,5,6}

                  A= {2,4,6}                     B= {3,6}

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