Generating random numbers is an important application of computer science and related fields. A common such generator in programming languages such as R and Python, is one that produces numbers randomly and uniformly distributed between 0 and 1. If done properly, the expected mean should be 0.5 and the standard deviation should be in the vicinity of 0.291. You decide to test this and ask your computer to randomly generate 50 numbers. The results are as follows: 0.0693609158042818, 0.817775198724121, 0.942621732363477, 0.269381876336411, 0.169348123250529, 0.0338956224732101, 0.178785004187375, 0.641665365546942, 0.0228777434676886, 0.00832482660189271, 0.392697197152302, 0.813880559289828, 0.37624845537357, 0.380812183720991, 0.264918377622962, 0.439334316411987, 0.457607151241973, 0.540707547217607, 0.665679829893634, 0. 112698937533423, 0.21836716751568, 0.787836347473785, 0.0978530396241695, 0.709830466192216, 0.217823043232784, 0.267943592974916, 0.504767951788381, 0.188586926786229, 0.439429325051606, 0.669819295872003, 0.240883231163025, 0.893264895770699, 0.882756386883557, 0.814063254510984, 0.633264608215541, 0.941087544662878, 0.693911424372345, 0.843702238984406, 0.384644460165873, 0.39112994633615, 0.58848182996735, 0.530477509833872, 0.97763589094393, 0.197649595327675, 0.844225987326354, 0.0950279189273715, 0.381151003297418, 0.045884121209383, 0.152623838977888, 0.774718186818063 Complete a hypothesis test to see if your number generator generates numbers with a mean that is different from the expected mean value of 0.5. Use the standard value for alpha.
Generating random numbers is an important application of computer science and related fields. A common such generator in programming languages such as R and Python, is one that produces numbers randomly and uniformly distributed between 0 and 1. If done properly, the expected mean should be 0.5 and the standard deviation should be in the vicinity of 0.291. You decide to test this and ask your computer to randomly generate 50 numbers. The results are as follows: 0.0693609158042818, 0.817775198724121, 0.942621732363477, 0.269381876336411, 0.169348123250529, 0.0338956224732101, 0.178785004187375, 0.641665365546942, 0.0228777434676886, 0.00832482660189271, 0.392697197152302, 0.813880559289828, 0.37624845537357, 0.380812183720991, 0.264918377622962, 0.439334316411987, 0.457607151241973, 0.540707547217607, 0.665679829893634, 0. 112698937533423, 0.21836716751568, 0.787836347473785, 0.0978530396241695, 0.709830466192216, 0.217823043232784, 0.267943592974916, 0.504767951788381, 0.188586926786229, 0.439429325051606, 0.669819295872003, 0.240883231163025, 0.893264895770699, 0.882756386883557, 0.814063254510984, 0.633264608215541, 0.941087544662878, 0.693911424372345, 0.843702238984406, 0.384644460165873, 0.39112994633615, 0.58848182996735, 0.530477509833872, 0.97763589094393, 0.197649595327675, 0.844225987326354, 0.0950279189273715, 0.381151003297418, 0.045884121209383, 0.152623838977888, 0.774718186818063 Complete a hypothesis test to see if your number generator generates numbers with a mean that is different from the expected mean value of 0.5. Use the standard value for alpha.
Holt Mcdougal Larson Pre-algebra: Student Edition 2012
1st Edition
ISBN:9780547587776
Author:HOLT MCDOUGAL
Publisher:HOLT MCDOUGAL
Chapter11: Data Analysis And Probability
Section: Chapter Questions
Problem 8CR
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Transcribed Image Text:Problem #2
Generating random numbers is an important application of computer science and related fields. A common such generator in
programming languages such as R and Python, is one that produces numbers randomly and uniformly distributed between 0 and
1. If done properly, the expected mean should be 0.5 and the standard deviation should be in the vicinity of 0.291. You decide to
test this and ask your computer to randomly generate 50 numbers. The results are as follows:
0.0693609158042818, 0.817775198724121, 0.942621732363477, 0.269381876336411, 0.169348123250529,
0.0338956224732101, 0.178785004187375, 0.641665365546942, 0.0228777434676886, 0.00832482660189271,
0.392697197152302, 0.813880559289828, 0.37624845537357, 0.380812183720991, 0.264918377622962,
0.439334316411987, 0.457607151241973, 0.540707547217607, 0.665679829893634, 0. 112698937533423,
0.21836716751568, 0.787836347473785, 0.0978530396241695, 0.709830466192216, 0.217823043232784,
0.267943592974916, 0.504767951788381, 0.188586926786229, 0.439429325051606, 0.669819295872003,
0.240883231163025, 0.893264895770699, 0.882756386883557, 0.814063254510984, 0.633264608215541,
0.941087544662878, 0.693911424372345, 0.843702238984406, 0.384644460165873, 0.39112994633615,
0.58848182996735, 0.530477509833872, 0.97763589094393, 0.197649595327675, 0.844225987326354.
0.0950279189273715, 0.381151003297418, 0.045884121209383, 0.152623838977888, 0.774718186818063
Complete a hypothesis test to see if your number generator generates numbers with a mean that is different from the expected
mean value of 0.5. Use the standard value for alpha.
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