Convert -15 degrees Celsius into degrees Fahrenheit using the formula $F=\frac{9}{5}C+32$Round your answer to one decimal place.

traquealwm
2022-08-16
Answered

Convert -15 degrees Celsius into degrees Fahrenheit using the formula $F=\frac{9}{5}C+32$Round your answer to one decimal place.

You can still ask an expert for help

Leroy Cunningham

Answered 2022-08-17
Author has **14** answers

Change $-{15}^{\circ}C$ into ${}^{\circ}F$

$F=\frac{9}{5}C+32$

put $c=-15$

$F=-\frac{9}{5}\times 15+32=32-27={5}^{\circ}F$

$\Rightarrow -{15}^{\circ}C={5}^{\circ}F$

Answ=5

$F=\frac{9}{5}C+32$

put $c=-15$

$F=-\frac{9}{5}\times 15+32=32-27={5}^{\circ}F$

$\Rightarrow -{15}^{\circ}C={5}^{\circ}F$

Answ=5

asked 2022-03-02

A person stands on a bathroom scale. The reading is 150 lb. After the person gets off the scale, the reading is 2 lb.

a) Is it possible to estimate the uncertainty in this measurement? If so, estimate it. If not, explain why not.

b) Is it possible to estimate the bias in this measurement? If so, estimate it. If not, explain why not.

a) Is it possible to estimate the uncertainty in this measurement? If so, estimate it. If not, explain why not.

b) Is it possible to estimate the bias in this measurement? If so, estimate it. If not, explain why not.

asked 2022-03-01

(a) What is a population?

A population is the complete set of measurements from every individual of interest.

A population is a subset of the complete set of measurements from every individual of interest.

A population is a numerical descriptive measure of the complete set of measurements from every individual of interest.

A population is a numerical descriptive measure of a subset of the complete set of measurements from every individual of interest.

(b) How do you get a simple random sample?

A simple random sample is accomplished by dividing the entire population into distinct subgroups based on a specific characteristic such as age, income, education level, and so on. All members of a subgroup share the specific characteristic. Draw random samples from each subgroup.

A simple random sample is accomplished by dividing the entire population into pre-existing segments. Make a random selection of segments. Include every member of each selected segment in the sample.

A simple random sample is accomplished by assigning numbers to members of the population and then using a table, calculator, or computer to select random numbers from the numbers assigned to the population members. Create the sample by using population members with numbers corresponding to those randomly selected.

A simple random sample is accomplished by using data from population members that are readily available.

A simple random sample is accomplished by numbering all members of the population sequentially. Then, from a starting point selected at random, include every kth member of the population in the sample.

(c) What is a sample statistic?

A sample statistic is the complete set of measurements from every individual of interest.

A sample statistic is a subset of the complete set of measurements from every individual of interest.

A sample statistic is a numerical descriptive measure of the complete set of measurements from every individual of interest.

A sample statistic is a numerical descriptive measure of a subset of the complete set of measurements from every individual of interest.

A population is the complete set of measurements from every individual of interest.

A population is a subset of the complete set of measurements from every individual of interest.

A population is a numerical descriptive measure of the complete set of measurements from every individual of interest.

A population is a numerical descriptive measure of a subset of the complete set of measurements from every individual of interest.

(b) How do you get a simple random sample?

A simple random sample is accomplished by dividing the entire population into distinct subgroups based on a specific characteristic such as age, income, education level, and so on. All members of a subgroup share the specific characteristic. Draw random samples from each subgroup.

A simple random sample is accomplished by dividing the entire population into pre-existing segments. Make a random selection of segments. Include every member of each selected segment in the sample.

A simple random sample is accomplished by assigning numbers to members of the population and then using a table, calculator, or computer to select random numbers from the numbers assigned to the population members. Create the sample by using population members with numbers corresponding to those randomly selected.

A simple random sample is accomplished by using data from population members that are readily available.

A simple random sample is accomplished by numbering all members of the population sequentially. Then, from a starting point selected at random, include every kth member of the population in the sample.

(c) What is a sample statistic?

A sample statistic is the complete set of measurements from every individual of interest.

A sample statistic is a subset of the complete set of measurements from every individual of interest.

A sample statistic is a numerical descriptive measure of the complete set of measurements from every individual of interest.

A sample statistic is a numerical descriptive measure of a subset of the complete set of measurements from every individual of interest.

asked 2022-03-28

Determine the level of measurement of the variable.

Livability rankings for cities

Choose the correct level of measurement.

a. Ratio

b. Nominal

c. Ordinal

d. Interval

Livability rankings for cities

Choose the correct level of measurement.

a. Ratio

b. Nominal

c. Ordinal

d. Interval

asked 2022-06-14

Who knows

If I define algebra $\mathcal{F}(A)$ generated by $A$, collection of subsets of $S$ (the universal set) as the intersection of $\mathcal{F}$, algebra superset of $A$:

$\mathcal{F}(A)=\bigcap _{algebra\text{}\mathcal{F}\supseteq A}\mathcal{F}$

What if $A$ is an infinite (either countable or uncountable) set? Algebra, unlike $\sigma $-algebra, guarantees being closed under finite Boolean operations. Here, finite(in the definition of algebra) and infinite(in the setting) confuses me. e.g. $A$ is the collection of intervals $(-\mathrm{\infty},x]$($x\in \mathbb{R}$) and $S=\mathbb{R}$, what $\mathcal{F}(A)$ be like? Any help will be appreciated!

If I define algebra $\mathcal{F}(A)$ generated by $A$, collection of subsets of $S$ (the universal set) as the intersection of $\mathcal{F}$, algebra superset of $A$:

$\mathcal{F}(A)=\bigcap _{algebra\text{}\mathcal{F}\supseteq A}\mathcal{F}$

What if $A$ is an infinite (either countable or uncountable) set? Algebra, unlike $\sigma $-algebra, guarantees being closed under finite Boolean operations. Here, finite(in the definition of algebra) and infinite(in the setting) confuses me. e.g. $A$ is the collection of intervals $(-\mathrm{\infty},x]$($x\in \mathbb{R}$) and $S=\mathbb{R}$, what $\mathcal{F}(A)$ be like? Any help will be appreciated!

asked 2022-06-22

I am trying to find an example that shows the image of a measurable function is not measurable.

Here is what I found: Let X={0,1} with $\sigma $-algebra $\{\mathrm{\varnothing},X\}$. Let $f:X\to X$ and f(x)=0. Then f(X)={0} is not measurable.

But how do we know that f is measurable in the first place? Obviously, we have ${f}^{-1}(\{\})=\{\}$. However, how can we say that ${f}^{-1}(X)={f}^{-1}(\{0,1\})=X=\{0,1\}$? I mean f is not surjective; it is impossible that f(x)=1.

Here is what I found: Let X={0,1} with $\sigma $-algebra $\{\mathrm{\varnothing},X\}$. Let $f:X\to X$ and f(x)=0. Then f(X)={0} is not measurable.

But how do we know that f is measurable in the first place? Obviously, we have ${f}^{-1}(\{\})=\{\}$. However, how can we say that ${f}^{-1}(X)={f}^{-1}(\{0,1\})=X=\{0,1\}$? I mean f is not surjective; it is impossible that f(x)=1.

asked 2022-04-13

For some measuring processes, the uncertainty is approximately proportional to the value of the measurement. For example, a certain scale is said to have an uncertainty of ±2%. An object is weighed on this scale.

a) Given that the reading is 100 g, express the uncertainty in this measurement in grams.

b) Given that the reading is 50 g, express the uncertainty in this measurement in grams.

a) Given that the reading is 100 g, express the uncertainty in this measurement in grams.

b) Given that the reading is 50 g, express the uncertainty in this measurement in grams.

asked 2022-05-14

Any help!

Let $\mathcal{F},\mathcal{G}$ be $\sigma $-algebras on a set $\mathrm{\Omega}$. We define the join and meet of $\mathcal{F}$ and $\mathcal{G}$ to be

$\mathcal{F}\vee \mathcal{G}:=\sigma (\mathcal{F}\cup \mathcal{G})$

and

$\mathcal{F}\wedge \mathcal{G}:=\mathcal{F}\cap \mathcal{G},$

respectively.

However, how is $\mathcal{F}\cap \mathcal{G}$ defined? Is it

$\begin{array}{}\text{(1)}& \{A:A\in \mathcal{F}\text{and}A\in \mathcal{G}\}\end{array}$

or

$\begin{array}{}\text{(2)}& \{A\cap B:A\in \mathcal{F}\text{and}B\in \mathcal{G}\}?\end{array}$

In the theory of sets, I think it should be (1). But wouldn't it make much more sense to define $\mathcal{F}\wedge \mathcal{G}$ to be (2)?

Note that (2) is not a $\sigma $-algebra (or am I missing something?), but the $\sigma $-algebra generated by (2) is actually $\mathcal{F}\vee \mathcal{G}$. On the other hand, (1) is itself a $\sigma $-algebra.

Let $\mathcal{F},\mathcal{G}$ be $\sigma $-algebras on a set $\mathrm{\Omega}$. We define the join and meet of $\mathcal{F}$ and $\mathcal{G}$ to be

$\mathcal{F}\vee \mathcal{G}:=\sigma (\mathcal{F}\cup \mathcal{G})$

and

$\mathcal{F}\wedge \mathcal{G}:=\mathcal{F}\cap \mathcal{G},$

respectively.

However, how is $\mathcal{F}\cap \mathcal{G}$ defined? Is it

$\begin{array}{}\text{(1)}& \{A:A\in \mathcal{F}\text{and}A\in \mathcal{G}\}\end{array}$

or

$\begin{array}{}\text{(2)}& \{A\cap B:A\in \mathcal{F}\text{and}B\in \mathcal{G}\}?\end{array}$

In the theory of sets, I think it should be (1). But wouldn't it make much more sense to define $\mathcal{F}\wedge \mathcal{G}$ to be (2)?

Note that (2) is not a $\sigma $-algebra (or am I missing something?), but the $\sigma $-algebra generated by (2) is actually $\mathcal{F}\vee \mathcal{G}$. On the other hand, (1) is itself a $\sigma $-algebra.