Answer:
Any value given up from not choosing the other options is the <u>opportunity cost</u>
Explanation:
The cost of opportunity is the alternative that you sacrifice when you choose an option.
It represent the benefits that you misses out on when choosing one alternative over another.
In this case, the cost of opportunity is to plant crops.
Answer:
True movies is pursuing an integration strategy.
Explanation:
"Integrated marketing is the process of delivering a consistent and relevant content experience to your audience across all channels. [...] The ultimate goal of integrated marketing is a consistent, customer-centred experience that delivers results for your brand."
Reference: NewsCred. “What Is Integrated Marketing?” Insights, 7 Oct. 2019
Answer:
$49,950
Explanation:
X = amount in account
Make (x times the interest rate) equal to the $9.99 you will need to earn to cover the fee.
.02%* x = 9.99
.0002x= 9.99 (Divide both sides by .0002)
x = $49,950
With such a small interest rate, you will need to have a large sum of money in order to earn enough to cover the fee.
Answer:
Check the explanation
Explanation:
1. Record the journals as shone, below:
Date Accounts title & explanation Debit (S) Credit(S) 2016 Research and development expense 2,200,000 ` Cash 2,200,000
(To record the expense incurred
on research and development)
2017 Research and development expense 800,000 ` ` Software and development costs 400.000
Cash 1,200,000
(To record the sc&ware
development costs incanted)
kindly check the answer to the second question in the attached image below
Answer:
Supervised and Unsupervised Learning:
a. Unsupervised learning
b. Supervised learning
3. Supervised learning
4. Unsupervised learning
Explanation:
The key difference between supervised machine learning and unsupervised machine learning is that with supervised machine learning there is a training dataset (labeled data) on which the algorithm is trained to predict patterns. With unsupervised machine learning on the other hand, there is no training data. So, the algorithm discovers patterns on itself without reference to another labeled data or training dataset.
